ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)

Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

About the Journal Editorial Board Publishing Department Sponsors and Supporters Opened special issues Společnost pro radioelektronické inženýrství [CZ]
About the Journal
Feature Articles
Editorial Board
Publishing Department
Society [CZ]
Log out
Your Profile
Administration

December 2024, Volume 33, Number 4 [DOI: 10.13164/re.2024-4]

Show all Hide all
RIS File
BibTeX

P. Han, Z. Wang, H. Liu, M. Gao, S. Fang [references] [full-text] [DOI: 10.13164/re.2024.0487] [Download Citations]
A Novel Balanced Nonreciprocal Bandpass Filter Based on Stepped-Impedance Resonator and Time-Modulated Resonator

A novel balanced nonreciprocal bandpass filter based on the stepped impedance resonator and the time-modulated resonator is proposed in this paper. The balanced nonreciprocal bandpass filter is fabricated on a single PCB board, and the compact structure is achieved through the gap coupling structure of the microstrip resonators. Utilizing the quarter-wavelength transformer and gap coupling structure, good isolation between RF and modulated signals is achieved without adding lumped elements. Relying on the efficient modulation circuit and the half-wavelength stepped impedance resonator’s inherent resonance characteristic, an excellent nonreciprocal characteristic of the differential signal and effective suppression of common-mode noise are achieved. A balanced microstrip nonreciprocal bandpass filter operating at the center frequency of 1.5 GHz is designed, simulated, and experimentally verified. The measured reverse isolation is greater than 20 dB with a bandwidth of 48.8 MHz. The measured forward differential-mode insertion loss is 3.7 dB at the center frequency of 1.5 GHz. In the range of 1.2–1.8 GHz, the measured common-mode noise suppression is larger than 60.4 dB.

  1. KORD, A., SOUNAS, D., ALÙ, A. Achieving full-duplex communication: Magnetless parametric circulators for full-duplex communication systems. IEEE Microwave Magazine, 2018, vol. 19, no. 1, p. 84–90. DOI: 10.1109/MMM.2017.2759638
  2. ZHOU, J., REISKARIMIAN, N., DIAKONIKOLAS, J., et al. Integrated full duplex radios. IEEE Communications Magazine, 2017, vol. 55, no. 4, p. 142–151. DOI: 10.1109/MCOM.2017.1600583
  3. GHAFFAR, F., BRAY, J., VASEEM, M., et al. Theory and design of tunable full-mode and half-mode ferrite waveguide isolators. IEEE Transactions on Magnetics, 2019, vol. 55, no. 8, p. 1–8. DOI: 10.1109/TMAG.2019.2910028
  4. FAY, C., COMSTOCK, R. Operation of the ferrite junction circulator. IEEE Transactions on Microwave Theory and Techniques, 1965, vol. 13, no. 1, p. 15–27. DOI: 10.1109/TMTT.1965.1125923
  5. ASHLEY, A., PSYCHOGIOU, D. RF co-designed bandpass filters/isolators using nonreciprocal resonant stages and microwave resonators. IEEE Transactions on Microwave Theory and Techniques, 2021, vol. 69, no. 4, p. 2178–2190. DOI: 10.1109/TMTT.2021.3061431
  6. CHANG, J., KAO, J., LIN, Y., et al. Design and analysis of 24-GHz active isolator and quasi-circulator. IEEE Transactions on Microwave Theory and Techniques, 2015, vol. 63, no. 8, p. 2638 to 2649. DOI: 10.1109/TMTT.2015.2442976
  7. CARCHON, G., NANWELAERS, B. Power and noise limitations of active circulators. IEEE Transactions on Microwave Theory and Techniques, 2000, vol. 48, no. 2, p. 316–319. DOI: 10.1109/22.821785
  8. WEI, F., YU, J., ZHANG, C., et al. Compact balanced dual-band BPFs based on short and open stub loaded resonators with wide common-mode suppression. IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, vol. 67, no. 12, p. 3043–3047. DOI: 10.1109/TCSII.2020.2994632
  9. PAN, B., FENG, W., SHI, Y., et al. High-performance wideband balanced bandpass filter based on transversal signal-interference techniques. IEEE Transactions on Plasma Science, 2020, vol. 48, no. 12, p. 4119–4126. DOI: 10.1109/TPS.2020.3035558
  10. WEI, F., ZHANG, C., ZENG, C., et al. A reconfigurable balanced dual-band bandpass filter with constant absolute bandwidth and high selectivity. IEEE Transactions on Microwave Theory and Techniques, 2021, vol. 69, no. 9, p. 4029–4040. DOI: 10.1109/TMTT.2021.3093907
  11. CHEN, X., YANG, T., CHI, P. Arbitrary-order balanced filter with reflection less characteristics for both common- and differential mode signals. IEEE Microwave and Wireless Components Letters, 2021, vol. 31, no. 6, p. 553–556. DOI: 10.1109/LMWC.2021.3068469
  12. WU, X., LIU, X., HICKLE, M., et al. Isolating bandpass filters using time-modulated resonators. IEEE Transactions on Microwave Theory and Techniques, 2019, vol. 67, no. 6, p. 2331–2345. DOI: 10.1109/TMTT.2019.2908868
  13. WU, X., NAFE, M., MELCON, A., et al. Frequency tunable nonreciprocal bandpass filter using time-modulated microstrip λg/2 resonators. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, vol. 68, no. 2, p. 667–671. DOI: 10.1109/TCSII.2020.3014499
  14. WU, X., NAFE, M., MELCON, A., et al. A nonreciprocal microstrip bandpass filter based on spatio-temporal modulation. In IEEE MTT-S International Microwave Symposium. Boston (MA, USA), 2019, p. 9–12. DOI: 10.1109/MWSYM.2019.8700732
  15. CHAUDHARY, G., JEONG, Y. Nonreciprocal bandpass filter using mixed static and time-modulated resonators. IEEE Microwave and Wireless Components Letters, 2022, vol. 32, no. 4, p. 297–300. DOI: 10.1109/LMWC.2021.3123306
  16. ALVAREZ-MELCON, A., WU, X., ZANG, J., et al. Coupling matrix representation of nonreciprocal filters based on time modulated resonators. IEEE Transactions on Microwave Theory and Techniques, 2019, vol. 67, no. 12, p. 4751–4763. DOI: 10.1109/TMTT.2019.2945756
  17. DUTTA, P., KUMAR, G. A., RAM, G. Numerical design of nonreciprocal bandpass filters with the aid of 3D coupling matrix for 5G bands. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, vol. 69, no. 7, p. 3334–3338. DOI: 10.1109/TCSII.2022.3157644
  18. SIMPSON, D., VRYONIDES, P., NIKOLAOU, S., et al. Nonreciprocal balanced bandpass filters with quasi-elliptic response. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, vol. 69, no. 12, p. 5159–5163. DOI: 10.1109/TCSII.2022.3202860
  19. HONG, J. Microstrip Filters for RF/Microwave Applications. 2nd ed., rev. Hoboken (NJ, USA): Wiley, 2011. DOI: 10.1002/9780470937297

Keywords: Balanced bandpass filters, nonreciprocal filter, stepped-impedance resonators, time-modulated resonators, common-mode noise suppression

Z. Zheng, J. Lai, Q. Zhang, J. Guo [references] [full-text] [DOI: 10.13164/re.2024.0494] [Download Citations]
Gridless Sparse Recovery-based Wind Speed Estimation for Wind-shear Detection Using Airborne Phased Array Radar

The accuracy of wind speed estimation is an important factor affecting wind-shear detection in airborne weather radar. Aiming at the problem that dictionary mismatch in the sparse recovery-based wind speed estimation leads to the performance degradation, this paper proposes a wind speed estimation method based on atomic norm minimization for airborne array weather radar. The method first constructs joint sparse recovery measurements by compensating multiple array element data with wind-shear orientation information, and then the wind speed is estimated on continuous parameter domain using atomic norm minimization with multiple compensated measurements. Simulation experiments demonstrate that the proposed method can effectively improve the accuracy of wind speed estimation under dictionary mismatch, and the performance is better than that of the existing sparse recovery-based method of wind speed estimation with the pre-set discretized dictionary.

  1. LUCCHI, G. A new airborne weather radar systems. Journal of Aircraft, 2015, vol. 19, no. 3, p. 239–245. DOI: 10.2514/6.1981-237
  2. BAXA, E. G. Airborne pulsed Doppler radar detection of low altitude windshear – A signal processing problem. Digital Signal Processing, 1991, vol. 1, no. 4, p. 186–197. DOI: 10.1016/1051-2004(91)90112-X
  3. LIN, C., ZHANG, K., CHEN, X., et al. Overview of low-level wind shear characteristics over Chinese mainland. Atmosphere, 2021, vol. 12, no. 5, p. 1–19. DOI: 10.3390/atmos12050628
  4. BRACALENTE, M., BRITT, L., JONES, R. Airborne Doppler radar detection of low altitude windshear. Journal of Aircraft, 1990, vol. 27, no. 2, p. 151–157. DOI: 10.2514/3.45911
  5. BAXA, E. G., LAI, Y. C., KUNKEL, M. W. New signal processing developments in the detection of low-altitude windshear with airborne Doppler radar. The Record of the 1993 IEEE National Radar Conference. Lynnfield (USA), 1993, p. 269 to 274. DOI: 10.1109/NRC.1993.270452
  6. RTCA. Minimum Operational Performance Standards (MOPS) for Airborne Weather Radar with Forward-Looking Windshear and Turbulent Detection Capability. 212 pages. [Online] Cited 2018-08-17. Available at: https://products.rtca.org/21dimut/
  7. BAXA, E. G., DESHPANDE, M. D. Signal Processing Techniques for Clutter Filtering and Wind Shear Detection (NASA Report). 44 pages. [Online] Cited 2013-09-06. Available at: https://ntrs.nasa.gov/citations/19910014841
  8. KLEMM, R. Principle of Space-time Adaptive Processing. 3rd ed. London (UK): IET Publishers, 2006. ISBN: 9780863415661
  9. ZHANG, Y., WANG, S. Remote relative wind velocity estimation using airborne Doppler radar and spectrum analysis. IEEE Transactions on Aerospace and Electronic Systems, 2001, vol. 47, no. 3, p. 1648–1667. DOI: 10.1109/TAES.2011.5937256
  10. LI, H., CHEN, Y., FENG, K., et al. Low-altitude windshear wind speed estimation method based on KASPICE-STAP. Sensors, 2023, vol. 23, no. 54, p. 1–17. DOI: 10.3390/s23010054
  11. OSEGUERA, R. M., BOWLES, R. L., ROBINSON, P. A. Airborne in situ computation of the wind shear hazard index. AIAA Journal, 1992. DOI: 10.2514/6.1992-291
  12. BAXA, E. G., LEE, J. The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radar. (NASA Report) 42 pages. [Online] Cited 2013-09-06. Available at: https://ntrs.nasa.gov/citations/19910018088
  13. BRITT, C., HARRAH, T., GRITTENDEN, L. Microburst hazard detection performance of the NASA experimental windshear radar system. In Aircraft Design, Systems, and Operations Meeting. Monterey (USA), 1993. DOI: 10.2514/6.1993-3943
  14. LI, H., ZHOU, M., GUO, Q., et al. Compressive sensing-based wind speed estimation for low-altitude wind-shear with airborne phased array radar. Multidimensional Systems and Signal Processing, 2018, vol. 29, no. 2, p. 719–732. DOI: 10.1007/s11045-016-0448-6
  15. CANDES, E. J., WAKIN, M. B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, vol. 25, no. 2, p. 21–30. DOI: 10.1109/MSP.2007.914731
  16. TANG, G., BHASKAR, B., SHAH, P., et al. Compressed sensing off the grid. IEEE Transactions on Information Theory, 2013, vol. 59, no. 11, p. 7465–7490. DOI: 10.1109/TIT.2013.2277451
  17. CANDES, E. J., FERNANDEZ-GRANDA, C. Towards a mathematical theory of super-resolution. Communications on Pure and Applied Mathematics, 2014, vol. 67, no. 6, p. 906–956. DOI: 10.1002/cpa.21455
  18. FAN, Y., WU, R., MENG, Z., et al. Wind shear signal simulation of the airborne weather radar. In Proceedings of the 2011 IEEE Radar Conference. Kansas City (USA), 2011, p. 710–713. DOI: 10.1109/RADAR.2011.5960630

Keywords: Wind speed estimation, wind-shear detection, airborne phased array weather radar

A. M. A. Mirza, A. Khawaja, S. Mughal, R. A. Butt [references] [full-text] [DOI: 10.13164/re.2024.0502] [Download Citations]
Distributed Symmetric Turbo Coded OFDM Scheme Incorporated with STBC-MIMO Antennas for Coded-Cooperative Wireless Communication under Wideband Noise Jamming Environment

This research paper proposes a novel anti-jamming technique based on a single-relay distributed symmetric Turbo coded orthogonal frequency division multiplexing (DSTC-OFDM) scheme. The stated scheme is incorporated with Alamouti space-time block code (STBC) multiple-input multiple-output (MIMO) 2×2 antennas for coded-cooperative wireless communication system under wideband noise jamming environment. As a suitable benchmark for comparison, a conventional symmetric Turbo coded OFDM (STC-OFDM) scheme incorporated with Alamouti STBC-MIMO (2×2) antennas is also developed for non-cooperative wireless communication system under the same jamming environment. Moreover, both the proposed MIMO schemes are compared with the corresponding single-antenna schemes. In this research, the modulation techniques employed are binary phase-shift keying and M-ary quadrature amplitude modulation while soft-demodulators are used at the destination node along with a joint iterative soft-input/soft-output decoding tech-nique. According to the Monte Carlo simulation results, the proposed DSTC-OFDM-MIMO (coded-cooperative) scheme with Alamouti-STBC (2×2) antennas outperforms the STC-OFDM-MIMO (non-cooperative) scheme by a gain that ranges between 0.5–6 dB for different jamming scenarios in the high SNR simulated region under the same conditions, i.e., the code rates Rc = 1/3 and data frame lengths l = 512 bits for both the proposed schemes. However, in the low SNR simulated region, the STC-OFDM-MIMO scheme shows similar performance as the DSTC-OFDM-MIMO scheme, under identical conditions. Furthermore, the proposed distributed scheme with STBC-MIMO (2×2) antennas incorporates both coding gain and cooperative diversity gain.

  1. PIRAYESH, H., ZENG, H. Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 2022, vol. 24, no. 2, p. 767–809. DOI: 10.1109/COMST.2022.3159185
  2. ZOU, Y., ZHU, J., WANG, X., et al. A survey on wireless security: Technical challenges, recent advances, and future trends. Proceedings of the IEEE, 2016, vol. 104, no. 9, p. 1727–1765. DOI: 10.1109/JPROC.2016.2558521
  3. ZHANG, L., RESTUCCIA, F., MELODIA, T., et al. Jam sessions: Analysis and experimental evaluation of advanced jamming attacks in MIMO networks. In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). 2019, p. 61–70. DOI: 10.1145/3323679.3326504
  4. JAGANNATH, A., JAGANNATH, J., DROZD, A. High rate reliability beamformer design for 2×2 MIMO-OFDM system under hostile jamming. In Proceedings of 2020 29th International Conference on Computer Communications and Networks (ICCCN). Honolulu (HI, USA), 2020, p. 1–9. DOI: 10.1109/ICCCN49398.2020.9209635
  5. LIANG, Y., REN, J., LI, T. Secure OFDM system design and capacity analysis under disguised jamming. IEEE Transactions on Information Forensics and Security, 2020, vol. 15, p. 738–752. DOI: 10.1109/TIFS.2019.2929449
  6. CHOI, H., PARK, S., LEE, H. N. Covert anti‐jamming communication based on Gaussian coded modulation. Applied Sciences (Switzerland), 2021, vol. 11, no. 9, p. 1–28. DOI: 10.3390/app11093759
  7. SKLAR, B. Digital Communications: Fundamentals and Applications. 2nd ed. Prentice Hall, 2017. ISBN: 978-0134724058
  8. MALEKI, M., MALIK, M., FOLKESSON, P., et al. Modeling and evaluating the effects of jamming attacks on connected automated road vehicles. In Proceedings of 2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC). Bejing (China), 2022, p. 12–23. DOI: 10.1109/PRDC55274.2022.00016
  9. SIDEK, O., YAHYA, A. Reed-Solomon coding for frequency hopping spread spectrum in jamming environment. American Journal of Applied Sciences, 2008, vol. 5, no. 10, p. 1281–1284. DOI: 10.3844/ajassp.2008.1281.1284
  10. BALDI, M., BIANCHI, M., CHIARALUCE, F., et al. Advanced coding schemes against jamming in telecommand links. In Proceedings of IEEE Military Communications Conference (MILCOM 2013). San Diego (CA, USA), 2013, p. 1220–1226. DOI: 10.1109/MILCOM.2013.208
  11. DAI, J., GUO, W., XU, D. The performance of LDPC coded frequency hopping system under follower jamming. In 2014 International Conference on Information and Communications Technologies (ICT 2014). Nanjing (China), 2014, p. 1–6. DOI: 10.1049/cp.2014.0613
  12. KAPLAN, A., CAN, M., ALTUNBAS, I., et al. Comparative performance evaluation of LDPC coded OFDM-IM under jamming attack. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 5, p. 6209–6224. DOI: 10.1109/TVT.2022.3232491
  13. TAVAKKOLI, R. Performance evaluation of turbo code system under jamming environment. Journal of Soft Computing and Decision Support Systems (JSCDSS), 2015, vol. 2, no. 3, p. 14–19. ISSN: 2289-8603
  14. PURWAR, A., JOSHI, D., CHAUBEY, V. K. GPS signal jamming and anti-jamming strategy. In 2016 IEEE Annual India Conference (INDICON). Bangalore (India), 2016, p. 1–6. DOI: 10.1109/INDICON.2016.7838933
  15. AHMED, E. S., HUSSEIN, A. A. Turbo code performance in the presence of jamming signals. In The 11th Scientific Conference of the University of Babylon. Baghdad (Iraq), 2009, p. 29–39. [Online]. Available at: https://www.researchgate.net/publication/215466329_Turbo_Code_Performance_in_the_Presence_of_Jamming_Signals
  16. BERROU, C., GLAVIEUX, A., THITIMAJSHIMA, P. Near Shannon limit error-correcting coding and encoding: Turbo-codes (1). In IEEE International Conference on Communications. Geneva (Switzerland), 1993, p. 1064–1070. DOI: 10.1109/icc.1993.397441
  17. MUGHAL, S., YANG, F., EJAZ, S., et al. Asymmetric turbo code for coded-cooperative wireless communication based on matched interleaver with channel estimation and multi-receive antennas at the destination. Radioengineering, 2017, vol. 26, no. 3, p. 878 to 889. DOI: 10.13164/re.2017.0878
  18. UMAR, R., YANG, F., XU, H. J., et al. Distributed turbo coded spatial modulation based on code matched interleaver for MIMO system. Wireless Networks, 2023, vol. 29, no. 5, p. 1995–2013. DOI: 10.1007/s11276-023-03256-1
  19. SENDONARIS, A., ERKIP, E., AAZHANG, B. User cooperation diversity. Part I. System description. IEEE Transactions on Communications, 2003, vol. 51, no. 11, p. 1927–1938. DOI: 10.1109/TCOMM.2003.818096
  20. SENDONARIS, A., ERKIP, E., AAZHANG, B. User cooperation diversity. Part II: Implementation aspects and performance analysis. IEEE Transactions on Communications, 2003, vol. 51, no. 11, p. 1939–1948. DOI: 10.1109/TCOMM.2003.819238
  21. LANEMAN, J. N., WORNELL, G. W., TSE, D. N. C. An efficient protocol for realizing cooperative diversity in wireless networks. In IEEE International Symposium on Information Theory - Proceedings. Washington (USA), 2001, p. 294–294. DOI: 10.1109/ISIT.2001.936157
  22. SOLIMAN, T., YANG, F., EJAZ, S., et al. Decode-and-forward polar coding scheme for receive diversity: A relay partially perfect retransmission for half-duplex wireless relay channels. IET Communications. 2017, vol. 11, no. 2, p. 185–191. DOI: 10.1049/iet-com.2016.0915
  23. SIMOENS, S., MUÑOZ-MEDINA, O., VIDAL, J., et al. Compress-and-forward cooperative mimo relaying with full channel state information. IEEE Transactions on Signal Processing, 2010, vol. 58, no. 2, p. 781–791. DOI: 10.1109/TSP.2009.2030622
  24. MESLEH, R. Y., HAAS, H., SINANOVIĆ, S., et al. Spatial modulation. IEEE Transactions on Vehicular Technology, 2008, vol. 57, no. 4, p. 2228–2241. DOI: 10.1109/TVT.2007.912136
  25. TASHATOV, N. N., SEKSEMBAYEVA, M. A., OVECHKIN, G. V., et al. Interference immunity and energy efficiency of digital communications systems in multipath channel with fading. Indonesian Journal of Electrical Engineering and Computer Science, 2022, vol. 27, no. 3, p. 1412–1418. DOI: 10.11591/ijeecs.v27.i3.pp1412-1418
  26. SU, W., SAFAR, Z., OLFAT, M., et al. Obtaining full-diversity space-frequency codes from space-time codes via mapping. IEEE Transactions on Signal Processing, 2003, vol. 51, no. 11, p. 2905 to 2916. DOI: 10.1109/TSP.2003.818200
  27. ALAMOUTI, S. M. A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 1998, vol. 16, no. 8, p. 1451–1458. DOI: 10.1109/49.730453
  28. HUO, P., CAO, L. Distributed STBC with soft information relay based on Gaussian approximation. IEEE Signal Processing Letters, 2012, vol. 19, no. 10, p. 599–602. DOI: 10.1109/LSP.2012.2206383
  29. MHEIDAT, H., UYSAL, M., AL-DHAHIR, N. Distributed space time block coded OFDM for relay-assisted transmission. In IEEE International Conference on Communications. Istanbul (Turkey), 2006, vol. 10, p. 4513–4519. DOI: 10.1109/ICC.2006.255350
  30. LIN, J. C. H., STEFANOV, A. Coded cooperation for OFDM systems. In 2005 International Conference on Wireless Networks, Communications and Mobile Computing, 2005, p. 7–10. DOI: 10.1109/WIRLES.2005.1549376
  31. UMAR, R., YANG, F., MUGHAL, S. BER performance of a polar coded OFDM over different channel models. In Proceedings of 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). Islamabad (Pakistan), 2018, p. 764–769. DOI: 10.1109/IBCAST.2018.8312308
  32. HUNTER, T. E., NOSRATINIA, A. Diversity through coded cooperation. IEEE Transactions on Wireless Communications, 2006, vol. 5, no. 2, p. 283–289. DOI: 10.1109/TWC.2006.1611050
  33. GAWANDE, P. D., LADHAKE, S. A. Optimal performance of convolution coded OFDM. In Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). Chennai (India), 2016, p. 335–338. DOI: 10.1109/WiSPNET.2016.7566150
  34. ELFITURI, M., HAMOUDA, W., GHRAYEB, A. A convolutional-based distributed coded cooperation scheme for relay channels. IEEE Transactions on Vehicular Technology, 2009, vol. 58, no. 2, p. 655–669. DOI: 10.1109/TVT.2008.927033
  35. TANG, L., YANG, F., ZHANG, S., et al. Joint iterative decoding for LDPC-coded multi-relay cooperation with receive multi antenna in the destination. IET Communications, 2013, vol. 7, no. 1, p. 1–12. DOI: 10.1049/iet-com.2012.0038
  36. HU, J., DUMAN, T. M. Low-density parity-check codes over wireless relay channels. IEEE Transactions on Wireless Communications, 2007, vol. 6, no. 9, p. 3384–3394. DOI: 10.1109/TWC.2007.06083
  37. UMAR, R., YANG, F., MUGHAL, S. Turbo coded OFDM combined with MIMO antennas based on matched interleaver for coded cooperative wireless communication. Information, 2017, vol. 8, no. 2, p. 1–16. DOI: 10.3390/info8020063
  38. SOLIMAN, T. H. M., YANG, F., EJAZ, S. Interleaving gains for receive diversity schemes of distributed turbo codes in wireless half-duplex relay channels. Radioengineering, 2015, vol. 24, no. 2, p. 481–488. DOI: 10.13164/re.2015.0481
  39. EJAZ, S., YANG, F. F. Jointly optimized Reed-Muller Codes for multi-level multi-relay coded-cooperative VANETS. IEEE Transactions on Vehicular Technology, 2016, vol. 66, no. 5, p. 4017–4028. DOI: 10.1109/TVT.2016.2604320
  40. UMAR, R., YANG, F. F., MUGHAL, S. Distributed Reed-Muller code with multiple relays for cooperative broadband wireless networks. Radioelectronics and Communications Systems, 2019, vol. 62, no. 9, p. 449–461. DOI: 10.3103/S0735272719090024
  41. MUGHAL, S., UMAR, R., YANG, F. F., et al. Distributed Reed‑Muller coded‑cooperative spatial modulation. Wireless Personal Communications, 2022, vol. 124, no. 3, p. 2785–2807. DOI: 10.1007/s11277-022-09489-1
  42. SOLIMAN, T., YANG, F., EJAZ, S. Decode and forward polar coding for half-duplex two-relay channels based on multilevel construction. International Journal of Electronics, 2016, vol. 104, no. 4, p. 539–554. DOI: 10.1080/00207217.2016.1218068
  43. UMAR, R., YANG, F., MUGHAL, S., et al. Distributed polar coded OFDM based on Plotkin’s construction for half duplex wireless communication. International Journal of Electronics, 2018, vol. 105, no. 7, p. 1097–1116. DOI: 10.1080/00207217.2018.1426118
  44. SOLIMAN, T., YANG, F. Cooperative punctured polar coding (CPPC) scheme based on Plotkin’s construction. Radioengineering, 2016, vol. 25, no. 3, p. 482–489. DOI: 10.13164/re.2016.0482
  45. CHRONOPOULOS, S. K., CHRISTOFILAKIS, V., TATSIS, G., et al. Performance of turbo coded OFDM under the presence of various noise types. Wireless Personal Communications, 2016, vol. 87, no. 4, p. 1319–1336. DOI: 10.1007/s11277-015-3055-1
  46. UMAR, R., YANG, F. F., XU, H. J., et al. Distributed polar code based on Plotkin's construction with MIMO antennas in frequency selective Rayleigh fading channels. Wireless Personal Communications, 2019, vol. 104, no. 1, p. 287–306. DOI: 10.1007/s11277-018-6020-y
  47. VAN DER MEULEN, E. C. Three-terminal communication channels. Advances in Applied Probability, 1971, vol. 3, no. 1, p. 120–154. DOI: 10.2307/1426331
  48. BENEDETTO, S., DIVSALAR, D., MONTORSI, G., et al. A soft input soft-output APP module for iterative decoding of concatenated codes. IEEE Communications Letters, 1997, vol. 1, no. 1, p. 22–24. DOI: 10.1109/4234.552145
  49. PICKHOLTZ, R. L., SCHILLING, D. L., MILSTEIN, L. B. Theory of spread-spectrum communications - A tutorial. IEEE Transactions on Communications, 1982, vol. 30, no. 5, p. 855 to 884. DOI: 10.1109/TCOM.1982.1095533
  50. SCHILLING, D. L., MILSTEIN, L. B., PICKHOLTZ, R. L., et al. Optimization of the processing gain of an M-ary DSSS communication system. IEEE Transactions on Communications, 1980, vol. 28, no. 8, p. 1389–1398. DOI: 10.1109/TCOM.1980.1094782
  51. THOMPSON, S. C., PROAKIS, J. G., ZEIDLER, J. R., et al. Constant envelope OFDM in multipath Rayleigh fading channels. In Proceedings of the IEEE Military Communications Conference MILCOM. Washington (USA), 2006, p. 1–7. DOI: 10.1109/MILCOM.2006.302187
  52. IEEE. IEEE STD 802.11A, Supplement to IEEE Standard for Information Technology - Telecommunications and Information Exchange between Systems - Local and Metropolitan Area Networks - Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY). IEEE, 1999, p. 1-102. DOI: 10.1109/IEEESTD.1999.90606
  53. DO, T. T., BJORNSON, E., LARSSON, E. G., et al. Jamming resistant receivers for the massive MIMO uplink. IEEE Transactions on Information Forensics and Security, 2018, vol. 13, no. 1, p. 210–223. DOI: 10.1109/TIFS.2017.2746007

Keywords: Alamouti space-time block coding, anti-jamming technique, distributed symmetric turbo code, multiple-input multiple-output, orthogonal frequency division multiplexing, wideband noise jamming

J. Xue, D. Z. Wang [references] [full-text] [DOI: 10.13164/re.2024.0519] [Download Citations]
Multi-Channel Differential Synchronous Demodulator for Linear Inductive Position Sensor

A multi-channel differential synchronous demodulator is proposed for third harmonics suppression in three-phase receiving coils based linear inductive position sensor, which is realized by taking envelope differences of every two induced signals on three-phase receiving coils as differential signals. The proposed demodulator includes a differential synchronous envelope detector (DSED), and a time division MUX (TDM). By using the DSED, the envelope differences are synchronously demodulated as differential signals. The TDM including a multi-channel selector, a MUX and a voltage follower, is used for independently outputting the corresponding differential signals for linear position calculation. The proposed demodulator was designed in a 180 nm CMOS process and verified by Spectre simulator. Simulation results show third harmonics of differential signals are improved by 17.4 dB, indicating the proposed demodulator could be a candidate for designing the high performance analog front-end of inductive position sensor.

  1. SITTITHAI, P., PHAEBUA, K., LERTWIRIYAPRAPA, T., et al. Magnetic field shaping technique for HF-RFID and NFC systems. Radioengineering, 2019, vol. 28, no. 1, p. 121–128. DOI: 10.13164/re.2019.0121
  2. YOO, K., SEO, J., BAN, J., et al. Study on development of torque and angle sensor for EPS. SAE International Journal of Passenger Cars - Electronic and Electrical Systems, 2012, vol. 5, no. 1, p. 292–296. DOI: 10.4271/2012-01-0941
  3. ASCHENBRENNER, B., ZAGAR, B. G. Analysis and validation of a planar high-frequency contactless absolute position sensor. IEEE Transactions on Instrumentation and Measurement, 2015, vol. 64, no. 3, p. 768–775. DOI: 10.1109/TIM.2014.2348631
  4. KHALID, M. A., SALEEM, M. M., BUKHARI, S. A. R., et al. Design and characterization of three-axis high-range inductive tactile force sensor utilizing magnetorheological elastomer for robotic surgical. IEEE Sensors Journal, 2023, vol. 23, no. 1, p. 247–255. DOI: 10.1109/JSEN.2022.3222930
  5. RAHAL, M., DEMOSTHENOUS, A. An ASIC front end for planar high-frequency contactless inductive position sensors. IEEE Transactions on Instrumentation and Measurement, 2009, vol. 58, no. 9, p. 3021–3030. DOI: 10.1109/TIM.2009.2016819
  6. RAHAL, M., DEMOSTHENOUS, A. A synchronous chopping demodulator and implementation for high-frequency inductive position sensors. IEEE Transactions on Instrumentation and Measurement, 2009, vol. 58, no. 10, p. 3693–3701. DOI: 10.1109/TIM.2009.2019314
  7. RAHAL, M., DEMOSTHENOUS, A. An integrated signal conditioner for high-frequency inductive position sensors. Measurement Science and Technology, 2010, vol. 21, p. 1–12. DOI: 10.1088/0957-0233/21/1/ 015203
  8. ZOU, Y. T., ZHI, X. K., ZHANG, J., et al. A high accuracy lock-in transceiver for speed over 8000-r/min contactless eddy-current angular position sensors. IEEE Transactions on Instrumentation and Measurement, 2021, vol. 70, p. 1–12. DOI: 10.1109/TIM.2021.3101313
  9. HOBEIN, D., DORISSEN, T., DURKOPP, K. Progress in automotive position sensors and introduction of the Hella inductive position sensor. SAE Technical Paper 2004-01-1115, 2004. DOI: 10.4271/2004-01-1115
  10. ZHANG, C., LI, Z. P., CHEN, J., et al. Design and research of a novel non-contact vertical inductive torque sensor. Measurement, 2021, vol. 177, p. 1–10. DOI: 10.1016/j.measurement.2021.109252
  11. LUGANI, L., AKERMI, Y., LAVAL, P., et al. High speed inductive position sensor for E-machines. 7 pages. [Online] Available at: http:// media.melexis.com
  12. MATANGA, L. M. Performance evaluation and comparison of different inductive sensor-based position sensing approaches. Master Thesis Report. Grenoble INP-Phelma, Grenoble, 2021.
  13. BRAJON, B. Development of a hybrid magnetic-inductance angular sensor with 360° range and stray field immunity. Master Thesis Report. Politecnico Di Torino, Rome, 2020.
  14. MAK, P. I., U SENG PAN, MARTINS, R. P. On the design of a programmable-gain amplifier with built-in compact DC-offset cancellers for very low-voltage WLAN systems. IEEE Transactions on Circuits and Systems-I: Regular Papers, 2008, vol. 55, no. 2, p. 496–509. DOI: 10.1109/TCSI.2007.910643
  15. FATEMI-BEHBAHANI, E., FARSHIDI, E., ANSARI-ASL, K. Analysis of residue probability density function and comparator offset error in pipeilined ADCs. Radioengineering, 2016, vol. 25, no. 1, p. 208–215. DOI: 10.13164/re.2016.0208
  16. KIM, S. Y., ABBASIZADEH, H., ALI, I., et al. An inductive 2-D position detection IC with 99.8% accuracy for automotive EMR gear control system. IEEE Transactions on Very Large Scale Integration Systems, 2017, vol. 25, no. 5, p. 1731–1741. DOI: 10.1109/TVLSI.2017.2651112
  17. CASANELLA, R., CASAS, O., PALLÀS-ARENY, R. Differential synchronous demodulator for modulating sensors and impedance measurements. Measurement Science and Technology, 2005, vol. 16, p. 1637–1643. DOI: 10.1088/0957-0233/16/8/014
  18. GONZALEZ-LANDAETA, R., COTA-RUIZ, J., SIFUENTES, E., et al. A fully differential synchronous demodulator for AC signals.
  19. RADIOENGINEERING, VOL. 33, NO. 4, DECEMBER 2024 525
  20. IEEE Transactions on Instrumentation and Measurement, 2020, vol. 69, no. 1, p. 35–44. DOI: 10.1109/TIM. 2019.2895481
  21. MERZ, N., KIRANON, W., WONGTACHATHUM, C., et al. A modified bipolar translinear cell with improved linear range and its applications. Radioengineering, 2012, vol. 21, no. 2, p. 736 to 745. ISSN: 1210-2512
  22. ZHANG, S. F., ZHOU, X., GAO, C., et al. A 130-dB CMRR instrumentation amplifier with common-mode replication. IEEE Journal of Solid-State Circuits, 2022, vol. 57, no. 1, p. 278–289. DOI: 10.1109/JSSC.2021.3090100
  23. HU, C. Y., DATLINGER, C., ZHONG, Z. M., et al. Rotor position detection based on magnetically coupled resonant principle and its implementation. IEEE Sensors Journal, 2021, vol. 21, no. 6, p. 7402–7411. DOI: 10.1109/JSEN.2021.3050806
  24. YE, L., YANG, M., XU, L. Rotor study of inductive angle sensor. In Int. Conf. on Mechatronics and Automation. Chengdu (China), 2012, p. 697–701. DOI: 10.1109/ICMA.2012.6283227

Keywords: Inductive position sensor, multi-channel, differential synchronous demodulator, third harmonics noise

D. Zhao, L. Hu, W. Xiong, H. Cai, Y. Qian, Y. Xing [references] [full-text] [DOI: 10.13164/re.2024.0526] [Download Citations]
On Optimization of RIS-Assisted Secure UAV-NOMA Communications with Finite Blocklength

The combination of reconfigurable intelligent surface (RIS) with the unmanned aerial vehicle (UAV) and the non-orthogonal multiple access (NOMA) has emerged as a promising technology in the sixth generation (6G) network. However, UAV downlinks are susceptible to potential eavesdropping attacks due to the open and broadcasting nature of wireless channels. In addition, transmitting information with infinite blocklength (IBL) is impractical in 6G applications. In this paper, we propose a secure RIS-assisted UAV communication system based on NOMA with finite blocklength (FBL) transmission. To maximize the average secrecy rate for all ground users under the mobility and power constraints of the UAV, we jointly optimize the phase shift of the RIS, the trajectory, the transmit power of the UAV, and the user scheduling. To solve the formulated non-convex problem, we first transform the optimization problem into four convex sub-problems, i.e., phase shift optimization, trajectory optimization, transmit power optimization, and user scheduling optimization. Then, an iterative algorithm is developed based on the successive convex approximation (SCA) to solve the four sub-problems. Numerical results show that the average secrecy rate for all ground users achieved with the proposed algorithm is higher than that achieved with the traditional algorithms.

  1. QIAN, Y.,WANG, F., LI, J., et al. User association and path planning for UAV-aided mobile edge computing with energy restriction. IEEE Wireless Communications Letters, 2019, vol. 8, no. 5, p. 1312–1315. DOI: 10.1109/LWC.2019.2913843
  2. CHEN, X., CHANG, Z., LIU, M., et al. UAV-IRS assisted covert communication: Introducing uncertainty via phase shifting. IEEE Wireless Communications Letters, 2024, vol. 13, no. 1, p. 103–107. DOI: 10.1109/LWC.2023.3321957
  3. BERA, A., MISRA, S., CHATTERJEE, C. QoE analysis in cache-enabled multi-UAV networks. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 6, p. 6680–6687. DOI: 10.1109/TVT.2020.2985933
  4. DING, Y., FENG, Y., LU, W., et al. Online edge learning offloading and resource management for UAV-assisted MEC secure communications. IEEE Journal of Selected Topics in Signal Processing, 2023, vol. 17, no. 1, p. 54–65. DOI: 10.1109/JSTSP.2022.3222910
  5. CHEN, X., ZHANG, N., TANG, J., et al. UAV-aided covert communication with a multi-antenna jammer. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 11, p. 11619–11631. DOI: 10.1109/TVT.2021.3112121
  6. HUANG, J., TIAN, G., ZHANG, J., et al. On unmanned aerial vehicles light show systems: Algorithms, software and hardware. Applied Sciences, 2021, vol. 11, no. 16, p. 1–19. DOI: 10.3390/app11167687
  7. YE, J., FU, C., CAO, Z., et al. Tracker meets night: A transformer enhancer for UAV tracking. IEEE Robotics and Automation Letters, 2022, vol. 7, no. 2, p. 3866–3873. DOI: 10.1109/LRA.2022.3146911
  8. JIANG, X., YANG, Z., ZHAO, N., et al. Resource allocation and trajectory optimization for UAV-enabled multi-user covert communications. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 2, p. 1989–1994. DOI: 10.1109/TVT.2021.3053936
  9. WANG, J.,WANG, X., GAO, R., et al. Physical layer security for UAV communications: A comprehensive survey. China Communications, 2022, vol. 19, no. 9, p. 77–115. DOI: 10.23919/JCC.2022.09.007
  10. ZHOU, X., YAN, S., WU, Q., et al. Intelligent reflecting surface (IRS)-aided covert wireless communications with delay constraint. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 1, p. 532–547. DOI: 10.1109/TWC.2021.3098099
  11. LIN, Y., SHU, F., DONG, R., et al. Enhanced-rate iterative beamformers for active IRS-assisted wireless communications. IEEE Wireless Communications Letters, 2023, vol. 12, no. 9, p. 1538–1542. DOI: 10.1109/LWC.2023.3281821
  12. LIU, X., YU, Y., LI, F., et al. Throughput maximization for RISUAV relaying communications. IEEE Transactions on Intelligent Transportation Systems, 2022, vol. 23, no. 10, p. 19569–19574. DOI: 10.1109/TITS.2022.3161698
  13. QIAN, Y., YANG, C., MEI, Z., et al. On joint optimization of trajectory and phase shift for IRS-UAV assisted covert communication systems. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 10, p. 12873–12883. DOI: 10.1109/TVT.2023.3271461
  14. SHU, F., TENG, Y., LI, J., et al. Enhanced secrecy rate maximization for directional modulation networks via IRS. IEEE Transactions on Communications, 2021, vol. 69, no. 12, p. 8388–8401. DOI: 10.1109/TCOMM.2021.3110598
  15. CUI, M., ZHANG, G., ZHANG, R. Secure wireless communication via intelligent reflecting surface. IEEE Wireless Communications Letters, 2019, vol. 8, no. 5, p. 1410–1414. DOI: 10.1109/LWC.2019.2919685
  16. FANG, S., CHEN, G., LI, Y. Joint optimization for secure intelligent reflecting surface assisted UAV networks. IEEE Wireless Communications Letters, 2021, vol. 10, no. 2, p. 276–280. DOI: 10.1109/LWC.2020.3027969
  17. PANG, X., ZHAO,N., TANG, J., et al. IRS-assisted secure UAV transmission via joint trajectory and beamforming design. IEEE Transactions on Communications, 2022, vol. 70, no. 2, p. 1140–1152. DOI: 10.1109/TCOMM.2021.3136563
  18. ZHAI, D., LI, H., TANG, X., et al. Height optimization and resource allocation for NOMA enhanced UAV-aided relay networks. IEEE Transactions on Communications, 2021, vol. 69, no. 2, p. 962–975. DOI: 10.1109/TCOMM.2020.3037345
  19. LIU, M., YANG, J., GUI, G. DSF-NOMA: UAV- assisted emergency communication technology in a heterogeneous internet of things. IEEE Internet of Things Journal, 2019, vol. 6, no. 3, p. 5508–5519. DOI: 10.1109/JIOT.2019.2903165
  20. CAI, Y., WEI, Z., HU, S., et al. Resource allocation and 3D trajectory design for power-efficient IRS-assisted UAV-NOMA communications. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 12, p. 10315–10334. DOI: 10.1109/TWC.2022.3183300
  21. MU, X., LIU, Y., GUO, L., et al. Intelligent reflecting surface enhanced multi-UAV NOMA networks. IEEE Journal on Selected Areas in Communications, 2021, vol. 39, no. 10, p. 3051–3066. DOI: 10.1109/JSAC.2021.3088679
  22. BEAK, J., HAN, S. I., HAN, Y. Optimal resource allocation for non-orthogonal transmission in UAV relay systems. IEEE Wireless Communications Letters, 2018, vol. 7, no. 3, p. 356–359. DOI: 10.1109/LWC.2017.2778073
  23. WANG, Y., ZHOU, X. ZHUANG, Z., et al. UAV-enabled secure communication with finite block length. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 12, p. 16309–16313. DOI: 10.1109/TVT.2020.3042791
  24. SINGH, S. K., AGRAWAL, K., SINGH, K., et al. Performance analysis and optimization of RSMA enabled UAV-aided IBL and FBL communication with imperfect SIC and SCI. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 6, p. 3714–3732. DOI: 10.1109/TWC.2022.3220785
  25. AGRAWAL, N., BANSAL, A., SINGH, K., et al. Finite block length analysis of RIS-assisted UAV-based multiuser IOT communication system with non-linear EH. IEEE Transactions on Communications, 2020, vol. 70, no. 5, p. 3542–3557. DOI: 10.1109/TCOMM.2022.3162249
  26. HAN, H., HUANG, Y., HU, H., et al. Mobile jammer enabled secure UAV communication with short packet transmission. AEU - International Journal of Electronics and Communications, 2022, vol. 157, p. 1–10. DOI: 10.1016/j.aeue.2022.154434
  27. YANG, W., SCHAEFER, R. F., POOR, H. V. Finite-block length bounds for wiretap channels. In IEEE International Symposium on Information Theory (ISIT). Barcelona (Spain), 2016, p. 3087–3091. DOI: 10.1109/ISIT.2016.7541867
  28. WANG, H., YANG, Q., DING, Z., et al. Secure short-packet communications for mission-critical IOT applications. IEEE Transactions on Wireless Communications, 2019, vol. 18, no. 5, p. 2565–2578. DOI: 10.1109/TWC.2019.2904968
  29. ZHANG, J., CHEN, X., LI, M., et al. Optimized throughput in covert millimeter-wave UAV communications with beam sweeping. IEEE Wireless Communications Letters, 2021, vol. 10, no. 4, p. 720–724. DOI: 10.1109/LWC.2020.3041637
  30. TANG, R., CHENG, J., CAO, Z. Joint placement design, admission control, and power allocation for NOMA-based UAV systems. IEEE Wireless Communications Letters, 2020, vol. 9, no. 3, p. 385–388. DOI: 10.1109/LWC.2019.2956702

Keywords: Information security, reconfigurable intelligent surface, unmanned aerial vehicle, NOMA, finite blocklength

G. Krupa, G. Budzyn [references] [full-text] [DOI: 10.13164/re.2024.0537] [Download Citations]
Optimizing Shortwave Wideband RF Amplifier: Study of Transmission Line Transformer Construction Methods

This paper presents a study of six physical transmission line transformers (TLTs) designed to provide wideband output matching for laterally diffused metal oxide semiconductor (LDMOS) transistors within a push-pull amplifier operating in the 1.8-30 MHz spectrum with an output power of 600 W. While the mathematical model of TLTs is well described in the literature, the impact of physical construction methods on impedance matching and real amplifier performance is more challenging to ascertain. This paper compares six different TLTs built on various ferrite cores and employing different implementations of transmission lines. Return loss below -14 dB was achieved from 1.65 to 37.4 MHz, with most of the tested transformers exhibiting return loss better than -10 dB up to 50 MHz. The study also presents the impact of transmission line implementation on impedance matching using both special-purpose low impedance coaxial cable and a combination of general-purpose coaxial cables connected in parallel. Comparison of three chosen transformers in a real RF amplifier shows that using parallel transmission lines can lead to a return loss comparable to that of a special-purpose coaxial cable, although at the cost of lower efficiency and output power. Second harmonic cancellation effect was also investigated for three transformers.

  1. ALBULET, M. RF Power Amplifiers. 1st ed. Atlanta (USA): Noble Publishing, 2001. Chapter: 2.7. DOI: 10.1049/SBEW030E_ch2
  2. LEFRAK, F. How RF Transformers Work and How They are Measured (Application Note). 16 pages. [Online] Cited 2024-03-24. Available at: https://www.minicircuits.com/app/AN20-001.pdf
  3. GUANELLA, G. New method of impedance matching in radiofrequency circuits. The Brown Boveri Review, 1944, p. 125–127.
  4. TRASK, C. Transmission line transformers: Theory, design and applications - Part 1. High Frequency Electronics, 2005, p. 46–52.
  5. SEVICK, J. Transmission Line Transformers Handbook. 1st ed. Costa Mesa (US): Amidon Associates, Inc., 1997. ISBN: 1884932185
  6. RUTHROFF, C. L. Some broad-band transformers. Proceedings of the IRE, 1959, vol. 47, no. 8, p. 1337–1342. DOI: 10.1109/JRPROC.1959.287200
  7. MCLEAN, J. Analysis of the equal-delay topology for transformers and hybrid networks. IEEE Transactions on Electromagnetic Compatibility, 2006, vol. 48, no. 3, p. 516–521. DOI: 10.1109/TEMC.2006.879354
  8. ROTHOLZ, E. Transmission-line transformers. IEEE Transactions on Microwave Theory and Techniques, 1981, vol. 29, no. 4, p. 327–331. DOI: 10.1109/TMTT.1981.1130352
  9. HORN, J., BOECK, G. Design and modeling of transmission line transformers. In Proceedings of the SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC). Foz do Iguacu (Brazil), 2003, p. 421–424. DOI: 10.1109/IMOC.2003.1244897
  10. MCLEAN, J. Coaxial versus bifilar transmission lines as constituents of transmission-line transformers and baluns. In IEEE Symposium on Electromagnetic Compatibility Signal/Power Integrity. Grand Rapids (MI, USA), 2023, p. 425–430. DOI: 10.1109/EMCSIPI50001.2023.10241692
  11. BIDGOIL, A. H., KHODAEI, M., TAYARANI, M. Multioctave power amplifier design using 9:1 planar impedance transformer. IEEE Microwave and Wireless Components Letters, 2021, vol. 31, no. 1, p. 45–48. DOI: 10.1109/LMWC.2020.3034389
  12. MUHAMED, M., ABBOUD, F., ALHARIRI, M. Increase the bandwidth of the power amplifier to 185% of the bandwidth by ferrite transformers. International Journal of Electronics Letters, 2021, vol. 10, no. 2, p. 169–176. DOI: 10.1080/21681724.2020.1870722
  13. HOU, J., CHEN, G. Y., YANG, L., et al. 200W broadband power amplifier design over 9kHz to 400MHz. In Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC). Guilin (China), 2023, p. 1–3. DOI: 10.1109/CSRSWTC60855.2023.10427539
  14. MOMENZADEH, M., AHMADI, A. A compact size, multi octave bandwidth power amplifier, using LDMOS transistors. In Iranian Conference on Electrical Engineering (ICEE). Mashhad (Iran), 2018, p. 1714–1719. DOI: 10.1109/ICEE.2018.8472428
  15. PROBST, A. C., HITZINGER, H., BOCHTLER, U. High frequency power amplifier for wideband wireless communication systems. In Kleinheubach Conference. Miltengerg (Germany), 2021, p. 1–4. DOI: 10.23919/IEEECONF54431.2021.9598443
  16. TELANGI, S. R., KHOT, S. Continuous wave solid state power amplifier for RF heating system. In International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). Kottayam (India), 2018, p. 1–4. DOI: 10.1109/ICCSDET.2018.8821156
  17. XU, J., TONG, Z., RIVAS-DAVILA, J. 1 kW MHz wideband class E power amplifier. IEEE Open Journal of Power Electronics, 2022, vol. 3, p. 84–92. DOI: 10.1109/OJPEL.2022.3146835
  18. MAJUMDER, C., MAJUMDER, B., GHOSH, B., et al. A kW range solid-state power amplifier design for OTH radar’s transmitter application. In IEEE Microwaves, Antennas, and Propagation Conference (MAPCON). Ahmedabad (India), 2023, p. 1–6. DOI: 10.1109/MAPCON58678.2023.10463952
  19. NXP SEMICONDUCTORS. MRF300AN, MRF300BN 300W CW, 1.8-250 MHz, 50 V, Data Sheet, Rev. 2, 06/2019. 42 pages. [Online] Cited 2024-06-02. Available at: https://www.nxp.com/docs/en/datasheet/MRF300AN.pdf
  20. TRASK, C. Transmission line transformers: Theory, design and applications - Part 2. High Frequency Electronics, 2006, p. 26–30.
  21. SMITH, M. R., LEES, J., TASKER, J. P., et al. A novel formulation for high efficiency modes in push-pull power amplifiers using transmission line baluns. IEEE Microwave and Wireless Components Letters, 2012, vol. 22, no. 5, p. 257–259. DOI: 10.1109/LMWC.2012.2191273
  22. AMPELON, NETHERLANDS. R 10032 LDMOS Bias Module (Report). 10 pages. [Online] Cited 2024-06-01. Available at: https://vhfdesign.com/data/pdf/R_10032.pdf
  23. ROSU, A. I. Bias Circuits for RF Devices. 25 pages. [Online] Cited 2024-06-01. Available at: https://www.qsl.net/va3iul/Bias/Bias_Circuits_for_RF_Devices.pdf
  24. FATU, R. A 600W Broadband HF Amplifier Using Affordable LDMOS Devices. [Online] Cited 2024-06-01. Available at: https://qrpblog.com/2019/10/a-600w-broadband-hf-amplifierusing-affordable-ldmos-devices/
  25. LAMBERT, W. H. Second-order distortion in CATV push-pull amplifiers. Proceedings of the IEEE, 1970, vol. 58, no. 7, p. 1057–1062. DOI: 10.1109/PROC.1970.7849
  26. SAHOO, K. A., SINGHAL K. H., RAWAT, K. Multioctave VHF push-pull power amplifier using coaxial balun. In IEEE Microwaves, Antennas, and Propagation Conference (MAPCON). Bangalore (India), 2022, p. 157–160. DOI: 10.1109/MAPCON56011.2022.10047401
  27. NIIDA, Y., SATO, M., OHKI, T., et al. A 0.6-2.1 GHz wideband GaN high-power amplifier using transmission-line-transformer-based differential-mode combiner with second-harmonic suppression. IEEE Transactions on Microwave Theory and Techniques, 2021, vol. 69, no. 3, p. 1675–1683. DOI: 10.1109/TMTT.2021.3053977
  28. QIU, R. BLF188XR 2-30 MHz 1000 W Power Amplifier, Application Lab Report, Rev 1.0, March 2013, 12 pages. [Online] Cited 2024-06-02. Available at:
  29. https://community.nxp.com/pwmxy87654/attachments/pwmxy87654/other/14020/1/BLF188XR_50V_1270W_CW_2-30MHz_CD-101-13.pdf

Keywords: Amplifier, shortwave, Transmission Line Transformer (TLT), wideband, LDMOS

Z.-J. Xu, S. Zhang, Z.-W. Liu, J.-L. Lin, X.-S. Huang, J.-Y. Hua [references] [full-text] [DOI: 10.13164/re.2024.0552] [Download Citations]
Covert Communication System Based on Walsh Modulation and Noise Carriers

This study proposes a covert communication system, in which a non-zero-mean normally distributed random process is used as a carrier, and its mean is modulated by a Walsh code carrying M covert bits. The number of combinations is so huge that it is difficult for malicious parties to decode the covert information, even if they are aware of the existence of the transmitting signal. The received signal is multiplied at the receiving end with each Walsh code, and the mean value is computed. The Walsh code corresponding to the largest mean value is the transmitter's modulation code, thus recovering the transmitted covert bits. The system's theoretical symbol error rate and bit error rate are derived under additive white Gaussian noise and quasi-static fading channels, respectively. Simulation results are very consistent with the theoretical derivation. Compared with other existing schemes, the proposed scheme has good security, flexibility, and BER performance, and is very suitable for IoT devices with limited resources and low transmission rate but high concealability requirements.

  1. SHEN, Y., ZHANG Y., JIANG, X. Secrecy, Covertness and Authentication in Wireless Communications: Physical Layer Security Approach. Cham (Switzerland): Springer, 2023. ISBN: 9783031384646
  2. YANG, N., WANG, L., GERACI, G., et al. Safeguarding 5G wireless communication networks using physical layer security. IEEE Communications Magazine, 2015, vol. 53, no. 4, p. 20–27. DOI: 10.1109/MCOM.2015.7081071
  3. MENG, R., CUI, Q., ZHOU, Z., et al. A steganography algorithm based on CycleGAN for covert communication in the Internet of Things. IEEE Access, 2019, vol. 7, p. 90579–90584. DOI: 10.1109/ACCESS.2019.2920956
  4. YENER, A., ULUKUS, S. Wireless physical-layer security: Lessons learned from information theory. Proceedings of the IEEE, 2015, vol. 103, no. 10, p. 1814–1825. DOI: 10.1109/JPROC.2015.2459592
  5. CHENG, X., AN, J., XIONG, Z., et al. Covert communications: A comprehensive survey. IEEE Communications Survey & Tutorials, 2023, vol. 25, no. 2, p. 1171–1198. DOI: 10.1109/COMST.2023.326391
  6. LIU, Z. H., LIU, J. J., ZENG, Y., et al. Covert wireless communications in IoT systems: Hiding information in interference. IEEE Wireless Communications, 2018, vol. 25, no. 6, p. 46–52. DOI: 10.1109/MWC.2017.1800070
  7. ILLI, E., QARAQE, M., ALTHUNIBAT, S., et al. Physical layer security for authentication, confidentiality, and malicious node detection: A paradigm shift in securing IoT networks. IEEE Communications Surveys & Tutorials, 2024, vol. 26, no. 1, p. 347–388. DOI: 10.1109/COMST.2023.3327327
  8. PAKRAVAN, S., CHOUINARD, J. Y., LI, X. W., et al. Physical layer security for NOMA systems: Requirements, issues, and recommendations. IEEE Internet of Things Journal, 2023, vol. 10, no. 24, p. 21721–21737. DOI: 10.1109/JIOT.2023.3296319
  9. JAISWAL, N., PANDEY, A., YADAV, S., et al. Physical layer security performance of NOMA-aided vehicular communications over Nakagami-m time-selective fading channels with channel estimation errors. IEEE Open Journal of Vehicular Technology, 2023, vol. 4, p. 72–100. DOI: 10.1109/OJVT.2022.3222187
  10. SHU, F., WANG, J. Z. Intelligent Reflecting Surface-Aided Physical-Layer Security. Cham (Switzerland): Springer, 2023. ISBN: 9783031418112
  11. ZANDER, S., ARMITAGE, G., BRANCH, P. A survey of covert channels and countermeasures in computer network protocols. IEEE Communications Surveys & Tutorials, 2007, vol. 9, no. 3, p. 44–57. DOI: 10.1109/COMST.2007.4317620
  12. AZERANG, S. F., SAMSAMI KHODADAD, F., FOROUZESH, M. Covert communication based on energy harvesting and cooperative jamming. Wireless Networks, 2022, vol. 28, p. 3729–3738. DOI: 10.1007/s11276-022-03082-x
  13. TA, H. Q., PHAM, Q. V., KHUONG, H. V., et al. Covert communication with noise and channel uncertainties. Wireless Networks, 2022, vol. 28, p. 161–172. DOI: 10.1007/s11276-021-02828-3
  14. SHEN, Y. L., ZHANG, Y. Y., JIANG, X. H. Secrecy, Covertness and Authentication in Wireless Communications: Physical Layer Security Approach. Cham (Switzerland): Springer, 2023. ISBN: 9783031384646
  15. CHEN, X., ZHENG, T. X., DONG, L. M., et. al. Enhancing MIMO covert communications via intelligent reflecting surface. IEEE Wireless Communications Letters, 2022, vol. 11, no. 1, p. 33–37. DOI: 10.1109/LWC.2021.3119687
  16. HU, L. T., BI, S. J., LIU, Q. J., et. al. Intelligent reflecting surface aided covert wireless communication exploiting deep reinforcement learning. Wireless Networks, 2023, vol. 29, p. 877–899. DOI: 10.1007/s11276-022-03037-2
  17. LI, M., TAO, X. F., LI, N., et. al. Energy-efficient covert communication with the aid of aerial reconfigurable intelligent surface. IEEE Communications Letters, 2022, vol. 26, no. 9, p. 2101–2105. DOI: 10.1109/LCOMM.2022.3183637
  18. HUANG, S. H., LIU, W. W., LIU, G. J., et al. A correlation based approach to detecting wireless physical covert channels. Computer Communications, 2021, vol. 176, p. 31–39. DOI: 10.1016/J.COMCOM.2021.05.017
  19. HU, M. Y., QIAO, S., JI, X. P. Wireless covert communication with polarization dirty constellation. Applied Sciences, 2023, vol. 13, no. 6, p. 1–17. DOI: 10.3390/APP13063451
  20. MA, S., ZHANG, Y. Q., SHENG, H. H., et. al. Optimal probabilistic constellation shaping for covert communications. IEEE Transactions on Information Forensics and Security, 2022, vol. 17, p. 3165–3178. DOI: 10.1109/TIFS.2022.3203310
  21. WANG, Y. D., YAN, S. H., YANG, W. W., et. al. Probabilistic accumulate-then-transmit in wireless-powered covert communications. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 12, p. 10393–10406. DOI: 10.1109/TWC.2022.3183892
  22. CEK, M. E., SAVACI, F. A. Stable non-Gaussian noise parameter modulation in digital communication. Electronics Letters, 2009, vol. 45, no. 24, p. 1256–1257. DOI: 10.1049/EL.2009.2280
  23. CEK, M. E. Covert communication using skewed alpha-stable distributions. Electronics Letters, 2015, vol. 51, no. 1, p. 116–118. DOI: 10.1049/EL. 2014.3323.
  24. XU, Z. J., GONG, Y., WANG, K., et al. Covert digital communication systems based on joint normal distribution. IET Communications, 2017, vol. 11, no. 8, p. 1282–1290. DOI: 10.1049/iet-com.2016.1333
  25. XU, Z. J., LU, W. D., GONG, Y., et al. A covert communication system using non-zero mean normal distributions. Radioengineering, 2020, vol. 29, no. 3, p. 580–588. DOI: 10.13164/RE.2020.0580
  26. BASAR, E. Noise modulation. IEEE Wireless Communications Letters, 2024, vol. 13, no. 3, p. 844–848. DOI: 10.1109/LWC.2023.3346471

Keywords: Normal distribution, Walsh code, bit error rate, covert communication, IoT

Y. Li, F. Shu, Y. Song, J. Wang [references] [full-text] [DOI: 10.13164/re.2024.0563] [Download Citations]
A Novel Tree Model-based DNN to Achieve a~High-Resolution DOA Estimation via Massive MIMO Receive Array

To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity. To address this problem, a multi-level tree-based DNN model (TDNN) is proposed as an alternative , where each level takes small-scale multi-layer neural networks (MLNNs) as nodes to divide the target angular interval into multiple sub-intervals, and each output class is associated to a MLNN at the next level. Then the number of MLNNs is gradually increasing from the first level to the last level, and so increasing the depth of tree will dramatically raise the number of output classes to improve the estimation accuracy. More importantly, this network is extended to make a multi-emitter DOA estimation. Simulation results show that the proposed TDNN performs much better than conventional DNN and root multiple signal classification algorithm (root-MUSIC) at extremely low signal-to-noise ratio (SNR) with massive multiple input multiple output (MIMO) receive array, and can achieve Cramer-Rao lower bound (CRLB). Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made a substantial performance enhancement over DNN and Root-MUSIC, and this gain grows as the number of emitters increases.

  1. HEATH, R. W., GONZALEZ-PRELCIC, N., RANGAN, S., et al. An overview of signal processing techniques for millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 2016, vol. 10, no. 3, p. 436–453. DOI: 10.1109/JSTSP.2016.2523924
  2. XU, L., WEN, F., ZHANG, X., et al. A novel unitary PARAFAC algorithm for joint DOA and frequency estimation. IEEE Communications Letters, 2019, vol. 23, no. 4, p. 660–663. DOI: 10.1109/LCOMM.2019.2896593
  3. CHEN, P., YANG, Z., CHEN, Z., et al. Reconfigurable intelligent surface aided sparse DOA estimation method with non-ULA. IEEE Signal Processing Letters, 2021, vol. 28, p. 2023–2027. DOI: 10.1109/LSP.2021.3116789
  4. ZHANG, R., XIA,W.,YAN, F., et al. A single-site positioning method based on TOA and DOA estimation using virtual stations in NLOS environment. China Communications, 2019, vol. 16, no. 2, p. 146–159. DOI: 10.12676/j.cc.2019.02.010
  5. CHENG, L., WU, Y., ZHANG, J., et al. Subspace identification for DOA estimation in massive/full-dimension MIMO systems: Bad data mitigation and automatic source enumeration. IEEE Transactions on Signal Processing, 2015, vol. 63, no. 22, p. 5897–5909. DOI: 10.1109/TSP.2015.2458788
  6. LI, B., ZHANG, X., LI, J., et al. DOA estimation of non-circular source for large uniform linear array with a single snapshot: Extended DFT method. IEEE Communications Letters, 2021, vol. 25, no. 12, p. 3843–3847. DOI: 10.1109/LCOMM.2021.3120211
  7. WEN, F., CUI, G., GACANIN, H., et al. Compressive sampling framework for 2D-DOA and polarization estimation in mm wave polarized massive MIMO systems. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 5, p. 3071–3083. DOI: 10.1109/TWC.2022.3215965
  8. SHU, F., QIN, Y., LIU, T., et al. Low-complexity and high-resolution DOA estimation for hybrid analog and digital massive MIMO receive array. IEEE Transactions on Communications, 2018, vol. 66, no. 6, p. 2487–2501. DOI: 10.1109/TCOMM.2018.2805803
  9. LI, S., LIU, Y., YOU, L., et al. Covariance matrix reconstruction for DOA estimation in hybrid massive MIMO systems. IEEE Wireless Communications Letters, 2021, vol. 9, no. 8, p. 1196–1200. DOI: 10.1109/LWC.2020.2985014
  10. HUANG, H., YANG, J., HUANG, H., et al. Deep learning for super resolution channel estimation and DOA estimation based massive MIMO system. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 9, p. 8549–8560. DOI: 10.1109/TVT.2018.2851783
  11. HU, D., ZHANG, Y., HE, L., et al. Low-complexity deep-learning based DOA estimation for hybrid massive MIMO systems with uniform circular arrays. IEEE Wireless Communications Letters, 2020, vol. 9, no. 1, p. 83–86. DOI: 10.1109/LWC.2019.2942595
  12. PAPAGEORGIOU, G. K., SELLATHURAI, M., ELDAR, Y. C., et al. Deep networks for direction-of-arrival estimation in low SNR. IEEE Transactions on Signal Processing, 2021, vol. 69, p. 3714–3729. DOI: 10.1109/TSP.2021.3089927
  13. WU, L., LIU, Z., HUANG, Z. Deep convolution network for direction of arrival estimation with sparse prior. IEEE Signal Processing Letters, 2019, vol. 26, no. 11, p. 1688–1692. DOI: 10.1109/LSP.2019.2945115
  14. CONG, J.,WANG, X., YAN, C., et al. CRB weighted source localization method based on deep neural networks in multi-UAV network. IEEE Internet of Things Journal, 2023, vol. 10, no. 7, p. 5747–5759. DOI: 10.1109/JIOT.2022.3150794
  15. YU, J., WANG, Y. Deep learning-based multipath DoAs estimation method for mm Wave massive MIMO systems in low SNR. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 6, p. 7480–7490. DOI: 10.1109/TVT.2023.3239402
  16. WU, X., YANG, X., JIA, X., et al. A grid less DOA estimation method based on convolutional neural network with Toeplitz prior. IEEE Signal Processing Letters, 2022, vol. 29, p. 1247–1251. DOI: 10.1109/LSP.2022.3176211
  17. ZHANG, M., ZHOU, Z. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, vol. 26, no. 8, p. 1819–1837. DOI: 10.1109/TKDE.2013.39
  18. XU, S., BRIGHENTE, A., CHEN, B., et al. Deep neural networks for direction of arrival estimation of multiple targets with sparse prior for line-of-sight scenarios. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 4, p. 4683–4696. DOI: 10.1109/TVT.2022.3224586
  19. ZHAN, X., SUN, Z., SHU, F., et al. Rapid phase ambiguity elimination methods for DOA estimator via hybrid massive MIMO receive array. Chinese Journal of Electronics, 2024, vol. 33, no. 1, p. 175–184. DOI: 10.23919/cje.2022.00.112
  20. KINGMA, D. P., BA, J. Adam: a method for stochastic optimization. arXiv, 2014, arXiv:1412.6980, p. 1–15. DOI: 10.48550/arXiv.1412.6980
  21. CHEN, M., MAO, X., WANG, X. Performance analysis of deep neural networks for direction of arrival estimation of multiple sources. IET Signal Processing, 2023, vol. 17, no. 3, p. 1–18. DOI: 10.1049/sil2.12178
  22. BARABELL, A. Improving the resolution performance of eigen structure-based direction-finding algorithms. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Boston (MA, USA), 1983, vol. 8, p. 336–339. DOI: 10.1109/ICASSP.1983.1172124

Keywords: DOA estimation, DNN, massive MIMO, multi-label learning

Q. Cheng, J. Bai, X. Wang, B. Shi, W. Gao, F. Shu [references] [full-text] [DOI: 10.13164/re.2024.0571] [Download Citations]
Two Power Allocation and Beamforming Strategies for Active IRS-aided Wireless Network via Machine Learning

In this paper, a system utilizing an active intelligent reflecting surface (IRS) to enhance the performance of wireless communication network is modeled, which has the ability to adjust power between base station (BS) and active IRS. We aim to maximize the signal-to-noise ratio (SNR) of the user by jointly designing power allocation (PA) factor, active IRS phase shift matrix, and beamforming vector of BS, subject to a total power constraint. To tackle this non-convex problem, we solve this problem by alternately optimizing these variables. The PA factor is designed via polynomial regression method in machine learning. BS beamforming vector and IRS phase shift matrix are obtained by Dinkelbach's transform and successive convex approximation methods. Then, we maximize achievable rate (AR) and use closed-form fractional programming (CFFP) method to transform the original problem into an equivalent form. This problem is addressed by iteratively optimizing auxiliary variables, BS and IRS beamformings. Thus, two iterative PA methods are proposed accordingly, namely maximizing SNR based on PA factor (Max-SNR-PA) and maximizing AR based on CFFP (Max-AR-CFFP). The former has a better rate performance, while the latter has a lower computational complexity. Simulation results show that the proposed algorithms can effectively improve the rate performance compared to fixed PA strategies, only optimizing PA factor, aided by passive IRS, and without IRS.

  1. PAN, C., REN, H., WANG, K., et al. Reconfigurable intelligent surfaces for 6G systems: Principles, applications, and research directions. IEEE Communications Magazine, 2021, vol. 59, no. 6, p. 14–20. DOI: 10.1109/MCOM.001.2001076
  2. DENG, N., SHI, X., SHENG, M., et al. Enhancing millimeter wave cellular networks via UAV-borne aerial IRS swarms. IEEE Transactions on Communications, 2024, vol. 72, no. 1, p. 524–538. DOI: 10.1109/TCOMM.2023.3320701
  3. YANG, L., YANG, Y., HASNA, M. O., et al. Coverage, probability of SNR gain, and DOR analysis of RIS-aided communication systems. IEEE Wireless Communications Letters, 2020, vol. 9, no. 8, p. 1268–1272. DOI: 10.1109/LWC.2020.2987798
  4. SHU, F., WU, X., HU, J., et al. Secure and precise wireless transmission for random-subcarrier-selection-based directional modulation transmit antenna array. IEEE Journal on Selected Areas in Communications, 2018, vol. 36, no. 4, p. 890–904. DOI: 10.1109/JSAC.2018.2824231
  5. WANG, W., ZHANG, W. Joint beam training and positioning for intelligent reflecting surfaces assisted millimeter wave communications. IEEE Transactions on Wireless Communications, 2021, vol.20, no. 10, p. 6282–6297. DOI: 10.1109/TWC.2021.3073140
  6. SHU, F., QIN, Y., LIU, T., et al. Low-complexity and high-resolution DOA estimation for hybrid analog and digital massive MIMO receive array. IEEE Transactions on Communications, 2018, vol. 66, no. 6, p. 2487–2501. DOI: 10.1109/TCOMM.2018.2805803
  7. WU, Q., ZHANG,R. Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts. IEEE Transactions on Communications, 2020, vol.68, no.3, p.1838–1851. DOI: 10.1109/TCOMM.2019.2958916
  8. LIU, R., WU, Q., RENZO, D.M., et al. A path to smart radio environments: An industrial viewpoint on reconfigurable intelligent surfaces. IEEE Wireless Communications, 2022, vol. 29, no. 1, p. 202–208. DOI: 10.1109/MWC.111.2100258
  9. SHI, W., WU, Q., WU, D., et al. Joint transmit and reflective beamforming design for active IRS-aided SWIPT systems. Chinese Journal of Electronics, 2024, vol. 33, no. 2, p. 536–548. DOI: 10.23919/cje.2022.00.287
  10. MUHAMMAD, H. A., FAISAL, A., IMRAN, R., et al. Pilots optimization and surface area effects on channel estimation in RIS aided MIMO system. Radioengineering, 2023, vol. 32, no. 2, p. 187–196. DOI: 10.13164/re.2023.0187
  11. WU,Q., ZHANG, R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Transactions on Wireless Communications, 2019, vol. 18, no. 11, p. 5394–5409. DOI: 10.1109/TWC.2019.2936025
  12. HUANG, C., MO, R., YUEN, C. Reconfigurable intelligent surface assisted multiuser MISO systems exploiting deep reinforcement learning. IEEE Journal on Selected Areas in Communications, 2020, vol. 38, no. 8, p. 1839–1850. DOI: 10.1109/JSAC.2020.3000835
  13. SHI, W., WU, Q., XIAO, F., et al. Secrecy throughput maximization for IRS-aided MIMO wireless powered communication networks. IEEE Transactions on Communications, 2022, vol. 70, no. 11, p. 7520–7535. DOI: 10.1109/TCOMM.2022.3212734
  14. PAN, C., REN, H., WANG, K., et al. Multicell MIMO communications relying on intelligent reflecting surfaces. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 8, p. 5218–5233. DOI: 10.1109/TWC.2020.2990766
  15. LIN, Y., SHU, F., DONG, R., et al. Enhanced-rate iterative beamformers for active IRS-assisted wireless communications. IEEE Wireless Communications Letters, 2023, vol. 12, no. 9, p. 1538–1542. DOI: 10.1109/LWC.2023.3281821
  16. ZHAO, W., WANG, G., ATAPATTU, S., et al. Is backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system? IEEE Communications Letters, 2020, vol. 24, no. 6, p. 1342–1346. DOI: 10.1109/LCOMM.2020.2980510
  17. LV, W., BAI, J., YAN, Q., et al. RIS-assisted green secure communications: Active RIS or passive RIS? IEEE Wireless Communications Letters, 2023, vol. 12, no. 2, p. 237–241. DOI: 10.1109/LWC.2022.3221609
  18. LI, Y., YOU, C., CHUN, Y. J., et al. Active-IRS aided wireless network: System modeling and performance analysis. IEEE Communications Letters, 2023, vol. 27, no. 2, p. 487–491. DOI: 10.1109/LCOMM.2022.3221116
  19. YANG, X., WANG, H., FENG, Y. Sum rate maximization for active RIS-aided uplink multi-antenna NOMA systems. IEEE Wireless Communications Letters, 2023, vol. 12, no. 7, p. 1149–1153. DOI: 10.1109/LWC.2023.3264516
  20. LI, X., YOU, C., KANG, Z., et al. Double-active-IRS aided wireless communication with total amplification power constraint. IEEE Communications Letters, 2023, vol. 27, no. 10, p. 2817–2821. DOI: 10.1109/LCOMM.2023.3298070
  21. ZHI, K., PAN, C., REN, H., et al. Active RIS versus passive RIS: Which is superior with the same power budget? IEEE Communications Letters, 2022, vol. 26, no. 5, p. 1150–1154. DOI: 10.1109/LCOMM.2022.3159525
  22. SHU, F., LIU, J., LIN, Y., et al. Three high-rate beamforming methods for active IRS-aided wireless network. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 11, p. 15052–15056. DOI: 10.1109/TVT.2023.3279840
  23. ZHU,Q., LI, M., LIU, R., et al. Joint beamforming designs for active reconfigurable intelligent surface: A sub-connected array architecture. IEEE Transactions on Communications, 2022, vol. 70, no. 11, p. 7628–7643. DOI: 10.1109/TCOMM.2022.3212749
  24. KANG, J., KIM, I., KIM, D. Joint TX power allocation and RX power splitting for SWIPT system with multiple nonlinear energy harvesting circuits. IEEE Wireless Communications Letters, 2019, vol. 8, no. 1, p. 53–56. DOI: 10.1109/LWC.2018.2851229
  25. LIU, H., ZHU, P., CHEN, Y., et al. Power allocation for downlink hybrid power line and visible light communication system. IEEE Access, 2020, vol.8, p.24145–24152.DOI:10.1109/ACCESS.2020.2970097
  26. LEE, H., MODIANO, E., LE, L. Distributed throughput maximization in wireless networks via random power allocation. IEEE Transactions on Mobile Computing, 2012, vol. 11, no. 4, p. 577–590. DOI: 10.1109/TMC.2011.58
  27. LU, W., GONG, Y., WU, J., et al. Simultaneous wireless information and power transfer based on joint subcarrier and power allocation in OFDM systems. IEEE Access, 2017, vol. 5, p. 2763–2770. DOI: 10.1109/ACCESS.2017.2671903
  28. WAN, S., SHU, F., LU, J., et al. Power allocation strategy of maximizing secrecy rate for secure directional modulation networks. IEEE Access, 2018, vol. 6, p. 38794–38801. DOI: 10.1109/ACCESS.2018.2815779
  29. SHU, F., LIU, X., XIA, G., et al. High-performance power allocation strategies for secure spatial modulation. IEEE Transactions on Vehicular Technology, 2019, vol. 68, no. 5, p. 5164–5168. DOI: 10.1109/TVT.2019.2905765
  30. HUANG, X., ANSARI, N. Joint spectrum and power allocation for multi-node cooperative wireless systems. IEEE Transactions on Mobile Computing, 2015, vol. 14, no. 10, p. 2034–2044. DOI: 10.1109/TMC.2015.2388478
  31. CHEN, L., HAN, S., MENG, W., et al. Power allocation for single-stream dual-hop full-duplex decode-and-forward MIMO relay. IEEE Communications Letters, 2016, vol. 20, no. 4, p. 740–743. DOI: 10.1109/LCOMM.2016.2525777
  32. SUN, Y., BABU, P., PALOMAR, D. Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Transactions on Signal Processing, 2017, vol. 65, no. 3, p. 794–815. DOI: 10.1109/TSP.2016.2601299
  33. SHEN, K., YU, W. Fractional programming for communication systems–Part I: Power control and beamforming. IEEE Transactions on Signal Processing, 2018, vol. 66, no. 10, p. 2616–2630. DOI: 10.1109/TSP.2018.2812733
  34. SHEN, K., YU, W. Fractional programming for communication systems–Part II: Uplink scheduling via matching. IEEE Transactions on Signal Processing, 2018, vol. 66, no. 10, p. 2631–2644. DOI: 10.1109/TSP.2018.2812748
  35. ZHANG, Z., DAI, L., CHEN, X., et al. Active RIS vs. passive RIS: Which will prevail in 6G? IEEE Transactions on Communications, 2023, vol. 71, no. 3, p. 1707–1725. DOI: 10.1109/TCOMM.2022.3231893
  36. ZHAO, Y., WANG, X., WANG, Y., et al. High-performance power allocation strategies for active IRS-aided wireless network. arXiv, 2023, p. 1–5. DOI: 10.48550/arXiv.2311.04032
  37. BISHOP, C. M. Pattern Recognition and Machine Learning. 1st ed., rev. New York (USA): Springer, 2007. ISBN: 9780387310732

Keywords: Active intelligent reflecting surface, achievable rate, power allocation, closed-form fractional programming

N. Karmous, M. Hizem, Y. Ben Dhiab, M. Ould-Elhassen Aoueileyine, R. Bouallegue, N. Youssef [references] [full-text] [DOI: 10.13164/re.2024.0583] [Download Citations]
Hybrid Cryptographic End-to-End Encryption Method for Protecting IoT Devices Against MitM Attacks

End-to-End Encryption (E2EE) plays an essential role in safeguarding user privacy and protecting sensitive data across various communication platforms, including messaging applications, email services, and Internet of Things (IoT) devices. This paper presents a Hybrid Cryptography-Based E2EE method implemented on a Software Defined Networking (SDN) infrastructure, to strengthen bidirectional data security between hosts and IoT devices via the non-secure Message Queuing Telemetry Transport (MQTT) port. By addressing the threat of Man-in-the-Middle (MitM) attacks, the proposed system ensures that only authorized users can decrypt transmitted messages. This paper thoroughly analyzes the implementation and advantages of our Hybrid Cryptography-Based E2EE method by comparing RSA and ECC encryption techniques. ECC-256 is favored for key generation, owing to its high efficiency and speed, measured at 0.4009 ms. Additionally, through a comparison of RSA, AES, and ChaCha20 algorithms, AES-256 emerges as the optimal encryption choice, demonstrating the fastest encryption and decryption times for publishing 0.2758 ms and 0.1781 ms, respectively and for subscribing, with encryption at 0.2542 ms and decryption at 0.1577 ms. Along with its minimal packet size and low resource consumption, our proposed Hybrid Cryptography-Based E2EE method, implemented on SDN infrastructure, validate it's effectiveness in securing digital communications within SDN environments compared to existing solutions.

  1. MENEGHELLO, F., CALORE, A., ZUCCHETTO, D., et al. IoT: Internet of threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet of Things Journal, 2019, vol. 6, no. 5, p. 8182–8201. DOI: 10.1109/JIOT.2019.2935189
  2. SONI, D., MAKWANA, A. A survey on MQTT: A protocol of Internet of Things (IoT). In Proceedings of the International Conference on Telecommunication, Power Analysis and Computing Techniques (ICTPACT). Chennai (India), 2017, p. 1–20.
  3. BEN DHIAB, Y., OULD-ELHASSEN AOUEILEYINE, M., ABDELKADER, M., et al. Edge-based human activity recognition: A novel approach using spectral analysis and deep learning. In Proceedings of the International Wireless Communications and Mobile Computing (IWCMC). Ayia Napa (Cyprus), 2024, p. 1734–1739. DOI: 10.1109/IWCMC61514.2024.10592539
  4. SARICA, A. K., ANGIN, P. Explainable security in SDN based IoT networks. Sensors, 2020, vol. 20, no. 24, p. 1–30. DOI: 10.3390/S20247326
  5. BHUIYAN, Z. A., ISLAM, S., ISLAM, M. M., et al. On the (in)security of the control plane of SDN architecture: A survey. IEEE Access, 2023, vol. 11, p. 1–33. DOI: 10.1109/ACCESS.2023.3307467
  6. SHAGHAGHI, A., KAAFAR, M. A., BUYYA, R., et al. Software defined network (SDN) data plane security: Issues, solutions, and future directions. Chapter in Handbook of Computer Networks and Cyber Security: Principles and Paradigms. Cham: Springer, 2020, p. 341–387. DOI: 10.1007/978-3-030-22277-2_14
  7. CONTI, M., DRAGONI, N., LESYK, V. A survey of man-in-the middle attacks. IEEE Communications Surveys & Tutorials, 2016, vol. 18, no. 3, p. 2027–2051. DOI: 10.1109/COMST.2016.2548426
  8. FEREIDOUNI, H., FADEITCHEVA, O., ZALAI, M. IoT and man-in-the-middle attacks. arXiv, 2023, p. 1–11. DOI: 10.48550/arXiv.2308.02479
  9. YEE, C.K., ZOLKIPLI, M.F. Review on confidentiality, integrity and availability in information security. Journal of Information and Communication Technology in Education, 2021, vol. 8, no. 2, p. 34–42. DOI: 10.37134/JICTIE.vol8.2.4.2021
  10. THAMBIRAJA, E., RAMESH, G., UMARANI, R. A survey on various most common encryption techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 2012, vol. 2, no. 7, p. 226–233.
  11. HALE, B., KOMLO, C. On end-to-end encryption. Cryptology ePrint Archive, 2022, p. 1–20. [Online] Available at: https://eprint.iacr.org/2022/449
  12. DIXIT, P., GUPTA, K., TRIVEDI, M. C., et al. Traditional and hybrid encryption techniques: A survey. Networking Communication and Data Knowledge Engineering, 2018, vol. 4, p. 31–46. DOI: 10.1007/978-981-10-4600-1_22
  13. SUSHMA, P., GOPAL, V. V. H. Armstrong number encryption standard for smart devices- An IoT based encryption algorithm. Dogo Rangsang Research Journal, 2022, vol. 12, no. 12, p. 36–42. ISSN: 2347-7180
  14. SUSHMA, P. Smart devices security with Armstrong number encryption standard algorithm using MQTT protocol-An IoT application. International Journal of Intelligent Systems and Applications in Engineering, 2024, vol. 12, no. 10, p. 45–51. ISSN: 2147-6799
  15. HINTAW, A.J., MANICKAM, S., KARUPPAYAH, S., et al. A robust security scheme based on enhanced symmetric algorithm for MQTT in the internet of things. IEEE Access, 2023, vol. 11, p. 43019–43040. DOI: 10.1109/ACCESS.2023.3267718
  16. LI-WEN, H., YANG, K., FU, L., et al. Dynamic encryption method for MQTT communication. Journal of Physics: ConferenceSeries,2024, vol. 2717, no. 1, p. 1–8. DOI: 10.1088/1742-6596/2717/1/012011
  17. KHAN, M. R., UPRETI, K., ALAM, M. I., et al. Analysis of elliptic curve cryptography & RSA. Journal of ICT Standardization, 2023, vol. 11, no. 4, p. 355–378. DOI: 10.13052/jicts2245-800X.1142
  18. ROUTIS, G., DAGAS, P., ROUSSAKI, I. Enhancing privacy in the internet of vehicles via hyperelliptic curve cryptography. Electronics, 2024, vol. 13, no. 4, p. 1–29. DOI: 10.3390/electronics13040730
  19. LIGHT, R. A. Mosquitto: Server and client implementation of the MQTT protocol. Journal of Open Source Software, 2017, vol. 2, no. 13, p. 1–2. DOI: 10.21105/joss.00265
  20. GALLA, L. K., KOGANTI, V. S., NUTHALAPATI, N. Implementation of RSA. In Proceedings of the International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). Kumaracoil (India), 2016, p. 730–733. DOI: 10.1109/ICCICCT.2016.7987922
  21. NIMBHORKAR, S. U., MALIK, L. G. A survey on elliptic curve cryptography (ECC). International Journal of Advanced Studies in Computers, Science and Engineering, 2012, vol. 1, no. 1, p. 1–5.
  22. GARG, S., RANA, M. K. A review on RSA encryption algorithm. International Journal of Engineering and Computer Science, 2016, vol. 5, no. 7, p. 17148–17151. DOI: 10.18535/ijecs/v5i7.07
  23. ABDULLAH, A.M. Advanced encryption standard (AES) algorithm to encrypt and decrypt data. Cryptography and Network Security, 2017, vol. 16, no. 1, p. 1–12.
  24. THARA, K. S. T., VALLALA, P. G. A survey of encryption algorithms in IoT. In Proceedings of the 17th SC@RUG 2019-2020. Groningen (Netherlands), 2020, p. 9–14. ISBN: 978-94-034-2766-9
  25. VAHDATI, Z., YASIN, S., GHASEMPOUR, A., et al. Comparison of ECC and RSA algorithms in IoT devices. Journal of Theoretical and Applied Information Technology, 2019, vol. 97, no.16, p.4293–4301. ISSN: 1992-8645
  26. SUAREZ-ALBELA, M., FERNANDEZ-CARAMES, T. M., FRAGA-LAMAS, P., et al. A practical performance comparison of ECC and RSA for resource-constrained IoT devices. In Proceedings of the Global Internet of Things Summit (GIoTS). Bilbao (Spain), 2018, p. 1–6. DOI: 10.1109/GIOTS.2018.8534575
  27. YU, H., KIM, Y. New RSA encryption mechanism using onetime encryption keys and unpredictable bio-signal for wireless communication devices. Electronics, 2020, vol. 9, no. 2, p. 1–10. DOI: 10.3390/electronics9020246
  28. KIM, K., CHOI, S., KWON, H., et al. PAGE-practical AES-GCM encryption for low-end microcontrollers. Applied Sciences, 2020, vol. 10, no. 9, p. 1–14. DOI: 10.3390/app10093131
  29. HAAKEGAARD, R., LANG, J. The Elliptic Curve Diffie Hellman (ECDH). [Online] Cited 2023-08-18. Available at: https://koclab.cs.ucsb.edu/teaching/ecc/project/2015Projects/Haakegaard+Lang.pdf
  30. KRAWCZYK, H. Cryptographic extraction and key derivation: The HKDF scheme. In Proceedings of the Annual Cryptology Conference (CRYPTO). Berlin (Germany), 2011, p. 631–648. DOI: 10.1007/978-3-642-14623-7_34
  31. IETF US Secure Hash Algorithms (SHA and SHA-based HMAC and HKDF). RFC 6234, 2011. DOI: 10.17487/RFC6234

Keywords: Software-Defined Networking (SDN), cyber security, Man-in-the-Middle (MitM), end-to-end encryption, Internet of Things (IoT)

Y. Cheng, K. X. Li, C. B. Xiu, J. X. Liu [references] [full-text] [DOI: 10.13164/re.2024.0593] [Download Citations]
Improved Generalized Compound Distributed Clutter Simulation Method

In modern radar systems, the use of generalized compound distributed models can more accurately describe the amplitude distribution characteristics of sea clutter, which is crucial for radar signal processing and sea target detection. However, traditional zero memory nonlinearity (ZMNL) method cannot simulate generalized compound distributed sea clutter with arbitrary shape parameters. To address this issue, an improved method for generating random variable was proposed, which combines the characteristics of the Gamma distribution and uses the additivity of its shape parameter. By increasing the branches for generating the Gamma distributed random variables, the Probability Density Function (PDF) of the Gamma function is transformed into a second-order nonlinear ordinary differential equation, and the Gamma distributed random variables with arbitrary shape parameters are solved. Finally, the Generalized Gamma (GГ) distributed random variables under arbitrary shape parameter can be obtained through specific nonlinear transformations. This method extends the shape parameters of generalized compound distributed clutter to general real numbers. Through comparative experiments with measured data, the generalized compound distributed model has strong universality and can more accurately represent measured data. Finally, the results of clutter simulation experiments also indicate that the proposed method is not only suitable for clutter simulation with non-integer or non-semi- integer shape parameters, but also further improves the fitting degree.

  1. DING, H., DONG, Y., LIU, N., et al. Overview and prospects of research on sea clutter property cognition (in Chinese). Journal of Radars, 2016, vol. 5, no. 5, p. 499–516. DOI: 10.12000/JR16069
  2. CARRETERO-MOYA, J., GISMERO-MENOYO, J., BLANCO DEL-CAMPO, A., et al. Statistical analysis of a high-resolution sea clutter database. IEEE Transactions on Geoscience and Remote Sensing, 2010, vol. 48, no. 4, p. 2024–2037. DOI: 10.1109/TGRS.2009.2033193
  3. BOCQUET, S. Analysis and simulation of low grazing angle X band coherent radar sea clutter using memoryless nonlinear transformations. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–13. DOI: 10.1109/TGRS.2022.3153632
  4. CHEN, Y., LI, Y., YANG, L., et al. An overview of statistical characteristics of ground-sea clutter (in Chinese). Air and Space Defense, 2020, vol. 3, no. 4, p. 44–51. ISSN: 2096-4641
  5. ZHOU, L., KUANG, H., ZHANG, Y., et al. Correction probability density distribution function of sea clutter based on logarithmic normal distribution (in Chinese). Radar and ECM, 2021, vol. 41, no. 1, p. 18–22. ISSN: 1009-0401
  6. ZHANG, K., SHUI, P., WANG, G. Non-coherent integration constant false alarm rate detectors against K-distributed sea clutter for coherent radar systems (in Chinese). Journal of Electronics & Information Technology, 2020, vol. 42, no. 7, p. 1627–1635. DOI: 10.11999/JEIT190441
  7. WATTS, S. Modeling and simulation of coherent sea clutter. IEEE Transactions on Aerospace and Electronic Systems, 2012, vol. 48, no. 4, p. 3303–3317. DOI: 10.1109/TAES.2012.6324707
  8. ANASTASSOPOULOS, V., LAMPROPOULOS, G. A. A generalized compound model for radar clutter. In IEEE National Radar Conference. Atlanta (GA, USA), 1994, p. 41–45. DOI: 10.1109/NRC.1994.328095
  9. ANASTASSOPOULOS, V., LAMPROPOULOS, G. A. High resolution radar clutter classification. In Proceedings International Radar Conference. Alexandria (VA, USA), 1995, p. 662–667. DOI: 10.1109/RADAR.1995.522628
  10. ANASTASSOPOULOS, V., LAMPROPOULOS, G., DROSOPOULOS, A., et al. High resolution radar clutter statistics. IEEE Transactions on Aerospace and Electronic Systems, 1999, vol. 35, no. 1, p. 43–60. DOI: 10.1109/7.745679
  11. HOU, X., MA, H., RUAN, H., et al. Simulation of correlated generalized compound distribution radar sea clutter (in Chinese). Journal of Telemetry, Tracking and Command, 2015, vol. 36, no. 2, p. 65–70.
  12. ZHAO, C., JIANG, S., MA, J., et al. Simulation of correlated generalized-compound-distributed radar sea clutter based on ZMNL transformation (in Chinese). Modern Radar, 2015, vol. 37, no. 2, p. 75–78.
  13. CONTE, E., LONGO, M., LOPS, M. Modelling and simulation of non-Rayleigh radar clutter. IEE Proceedings-F: Radar & Signal Processing, 1991, vol. 138, no. 2, p. 121–130. DOI: 10.1049/ip-f2.1991.0018
  14. ZHU, J., TANG, J. K-distribution clutter simulation methods based on improved ZMNL and SIRP (in Chinese). Journal of Radar, 2014, vol. 3, no. 5, p. 533–540. DOI: 10.3724/SP.J.1300.2014.13124
  15. FU, J., ZHOU, G., CHEN, H. Improved method of Pareto clutter simulation based on ZMNL (in Chinese). Journal of Projectiles, Rockets, Missiles and Guidance, 2019, vol. 39, no. 4, p. 19–21+28. DOI: 10.15892/j.cnki.djzdxb.2019.04.005
  16. BAKKER, R., CURRIE, B. The Dartmouth Database. Available at: http://soma.ece.mcmaster.ca/ipix/dartmouth/index.html
  17. XIE, L., CHEN, Z., JIANG, C., et al. Simulation of the correlated generalized compound distribution wide band radar clutter using ZMNL method (in Chinese). Journal of Signal Processing, 2009, vol. 25, no. 9, p. 1463–1468. ISSN: 1003-0530
  18. MARQUES, F. J., LOINGEVILLE, F. Improved near-exact distributions for the product of independent generalized gamma random variables. Computational Statistics & Data Analysis, 2016, vol. 102, p. 55–66. DOI: 10.1016/j.csda.2016.04.004
  19. YANG, R., HAN, J., WU, F., et al. Research on simulating method of optical intensity scintillation series with double generalized gamma distribution in atmospheric turbulence (in Chinese). Acta Photonica Sinica, 2021, vol. 50, no. 10, p. 332–337. ISSN: 1004-4213
  20. TANG, W., YUAN, Z., ZHAO, B. Research on radar clutter modeling and evaluation methods (in Chinese). Fire Control Radar Technology, 2022, vol. 51, no. 1, p. 87–91. DOI: 10.19472/j.cnki.1008-8652.2022.01.017
  21. LI, H. C., HONG, W., WU, Y. R., et al. On the empirical-statistical modeling of SAR images with generalized gamma distribution. IEEE Journal of Selected Topics in Signal Processing, 2011, vol. 5, no. 3, p. 386–397. DOI: 10.1109/JSTSP.2011.2138675
  22. ROSENBERG, L., WATTS, S., GRECO, M. S. Modeling the statistics of microwave radar sea clutter. IEEE Aerospace Electronic Systems Magazine, 2019, vol. 34, no. 10, p. 44–75. DOI: 10.1109/MAES.2019.2901562
  23. WU, Y., JING, F., WANG, L. A hybrid-estimation method of clutter distribution models based on MOM and least square method. Journal of Ordnance Equipment Engineering, 2017, vol. 38, no. 6, p. 95–98. ISSN: 2096-2304
  24. COMBES, C., NG, H. K. T. On parameter estimation for Amoroso family of distributions. Mathematics and Computers in Simulation, 2022, vol. 191, p. 309–327. DOI: 10.1016/j.matcom.2021.07.004
  25. OKAGBUE, H., ADAMU, M. O., ANAKE, T. A. Closed form expressions for the quantile function of the Chi square distribution using the hybrid of quantile mechanics and spline interpolation. Wireless Personal Communications, 2020, vol. 115, no. 3, p. 2093 to 2112. DOI: 10.1007/s11277-020-07672-w
  26. OKAGBUE, H., ADAMU, M. O., ANAKE, T. A. Approximations for the inverse cumulative distribution function of the gamma distribution used in wireless communication. Heliyon, 2020, vol. 6, no. 11, p. 240–844. DOI: 10.1016/j.heliyon.2020.e05523

Keywords: Clutter simulation, generalized Gamma distribution, zero memory nonlinearity (ZMNL), Gamma distribution

Q. Zhang, Q. Q. Chen, G. Xu, J. R. Chi [references] [full-text] [DOI: 10.13164/re.2024.0603] [Download Citations]
ISAR High Resolution Imaging Algorithm Based on Weighted Adaptive Mixed Norm

Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, this paper proposes a high resolution imaging algorithm for ISAR based on weighted adaptive mixed norm. By weighting against l_2,0 mixed norm term, an improved model of the sparse constraint ISAR signal is proposed. The model effectively distinguishes the signal and noise by adding the weight coefficient, and improves the reconstruction accuracy of the strong scattering center. Meanwhile, the weight coefficients in this improved model can be iteratively updated in each cycle to improve the image reconstruction accuracy. The optimization model takes advantage of mixed norm to achieve fast convergence in the operation, and adopts conjugate gradient descent method and fast Fourier transform operation in the solution, which simplifies the solving process of the optimization problem and improves the operation efficiency of the algorithm. Simulation data and measured data verify the effectiveness of the proposed method.

  1. ZENG, C. Z., ZHU, W. G., JIA, X. Bistatic inverse synthetic aperture radar imaging method for the high-speed motion target. Journal of Applied Remote Sensing, 2018, vol. 12, no. 4, p. 1–17. DOI: 10.1117/1.jrs.12.045012
  2. YANG, S., LI, S., FAN, H., et al. High resolution ISAR imaging of maneuvering targets based on azimuth adaptive partitioning and compensation function estimation. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–15. DOI: 10.1109/TGRS.2023.3334770
  3. ZHOU, P., ZHANG, G., YANG, W. A review of ISAR imaging technology. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Chongqing (China), 2020, p. 664–668. DOI: 10.1109/ICIBA50161.2020.9277180
  4. NEPAL, R., CAI, J., YAN, Z. Micro-Doppler radar signature identification within wind turbine clutter based on short-CPI airborne radar observations. IET Radar, Sonar and Navigation, 2015, vol. 9, no. 9, p. 1268–1275. DOI: 10.1049/iet-rsn.2015.0111
  5. ZHU, H. S., HU, W. H., GUO, B. F., et al. A bistatic inverse synthetic aperture radar sparse aperture high‐resolution imaging algorithm with migration compensation. IET Radar, Sonar and Navigation, 2022, vol. 16, no. 12, p. 1949–1962. DOI: 10.1049/rsn2.12309
  6. WU, K., CUI, W., XU, X. Super resolution radar imaging via peak search and compressed sensing. IEEE Geoscience and Remote Sensing Letters,
  7. 2022, vol. 19, p. 1–5. DOI:10.1109/LGRS.2022.3184067
  8. XU, G., ZHANG, B. J., CHEN, J. L., et al. Sparse inverse synthetic aperture radar imaging using structured low-rank
  9. method. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–12. DOI: 10.1109/TGRS.2021.3118083
  10. JI, B., ZHAO, B., WANG, Y., et al. SA-ISAR imaging algorithm based on the gradient signal recovery method. The Journal of Engineering, 2019,
  11. no. 20, p. 6577–6581. DOI: 10.1049/joe.2019.0284
  12. CHEN, Q. Q., XU, G., ZHANG, L., et al. Three-dimensional interferometric inverse synthetic aperture radar imaging with limited pulses by exploiting joint sparsity. IET Radar, Sonar and Navigation, 2015, vol. 9, no. 6, p. 692–701. DOI: 10.1049/ietrsn.2014.0275
  13. SONG, D. Y., CHEN, Q. Q., LI, K. Z. An adaptive sparse constraint ISAR high resolution imaging algorithm based on mixed norm. Radioengineering, 2022, vol. 31, no. 4, p. 477–485. DOI: 10.13164/re.2022.0477
  14. CHENG, P., CHEN, W., CHENG, J., et al. A fast ISAR imaging method based on strategy weighted CAMP algorithm. IEEE Sensors Journal, 2022, vol. 22, no. 17, p. 17022–17030. DOI: 10.1109/JSEN.2022.3192534
  15. CHEN, R., JIANG, Y., LIU, Z., et al. A novel spaceborne ISAR imaging approach for space target with high-order translational motion compensation and spatial variant MTRC correction. International Journal of Remote Sensing, 2023, vol. 44, no. 21, p. 6549–6578. DOI: 10.1080/01431161.2023.2272600
  16. CHEN, J. Y., XU, Z. H., XIAO, S. P. Irregular subarray design strategy based on weighted L1 norm iterative convex optimization. IEEE Antennas and Wireless Propagation Letters, 2022, vol. 21, no. 2, p. 376–380. DOI: 10.1109/LAWP.2021.3132001
  17. XU, Z. B., CHANG, X. Y., XU, F. M., et al. L1/2 regularization: A thresholding representation theory and a fast solver. IEEE Transactions on Neural Networks and Learning Systems, 2012, vol. 23, no. 7, p. 1013–1027. DOI: 10.1109/TNNLS.2012.2197412
  18. CANDES, E. J., WAKIN, M. B., BOYD, S. P. Enhancing sparsity by reweighted l1 minimization. Journal of Fourier Analysis and Applications, 2008, vol. 14, no. 5-6, p. 877–905. DOI: 10.1007/s00041-008-9045-x
  19. ZHANG, W. T., LOU, S. T., FENG, D. Z. Adaptive quasi-Newton algorithm for source extraction via CCA approach. IEEE Transactions on Neural Networks and Learning Systems, 2014, vol. 25, no. 4, p. 677–689. DOI: 10.1109/TNNLS.2013.2280285
  20. VAN DER HEIDE, O., SBRIZZI, A., VAN DER BERG, C. A. T. Accelerated MR-STAT reconstructions using sparse Hessian approximations. IEEE Transactions on Medical Imaging, 2020, vol. 39, no. 11, p. 3737–3748. DOI: 10.1109/TMI.2020.3003893
  21. PONOMAREV, A., PONOMAREVA, O., SMIRNOVA, N. Evolution of one-dimensional and two-dimensional discrete Fourier transform. In 24th International Conference on Digital Signal Processing and its Applications (DSPA). Moscow (Russian Federation), 2022, p. 1–6. DOI: 10.1109/DSPA53304.2022.9790768
  22. RONG, J., WANG, Y., HAN, T. Iterative optimization-based ISAR imaging with sparse aperture and its application in interferometric ISAR imaging. IEEE Sensors Journal, 2019, vol. 19, no. 19, p. 8681–8693. DOI: 10.1109/JSEN.2019.2923447
  23. WANG, Z. J., BADIU, M. A., COON, J. P. On the value of information and mean squared error for noisy Gaussian models. IEEE Communications Letters, 2022, vol. 26, no. 9, p. 2023–2026. DOI: 10.1109/LCOMM.2022.3188368
  24. LI, D., ZHAN, M. Y., LIU, H. Q., et al. A robust translational motion compensation method for ISAR imaging based on keystone transform and fractional Fourier transform under low SNR environment. IEEE Transactions on Aerospace and Electronic Systems, 2017, vol. 53, no. 5, p. 2140–2156. DOI: 10.1109/TAES.2017.2683599

Keywords: Inverse Synthetic Aperture Radar (ISAR), weight coefficient, sparse constraint, regularization coefficient, l_2,0 mixed norm

Z. Zhu, Z. Chen [references] [full-text] [DOI: 10.13164/re.2024.0612] [Download Citations]
Unequal Single-Ended-to-Balanced Power Divider with Enhanced Input-Matching Bandwidth

To reduce the circuit size of a single-ended-to-balanced power divider, a section of coupled line with a short-circuited terminal is applied. In this structure, two transmission zeros are generated, which realizes a wide input-matching bandwidth without extra absorbing branches. The enhanced input matching bandwidth can decrease the effect of the reflection wave power on the operation of the preceding active stage in the RF front end. Equations are derived to support the power transmission in a specific power division ratio. To verify the proposed structure, a single-ended-to-balanced power divider operating at 2.0 GHz is designed, fabricated, and measured. A wide matching bandwidth under −10 dB covers from 0.292 to 3.966 GHz with a fractional bandwidth (FBW) of 183.7%. Simulation and measurement results are in good agreement.

  1. SUN, Y., ZHANG, J., MEI, P., et al. Tri-band dual-polarized shared-aperture antenna arrays with wide-angle scanning and low profile for 5G base stations. IEEE Transactions on Antennas and Propagation, 2024, vol. 72, no. 3, p. 2455–2467. DOI: 10.1109/TAP.2024.3358611
  2. NIU, W., SUN, B., HUANG, X. A filtering and electromagnetic transparent antenna for triple-band aperture-shared base station antenna array. IEEE Antennas and Wireless Propagation Letters, 2024, vol. 23, no. 1, p. 244–248. DOI: 10.1109/LAWP.2023.3322414
  3. KIM, Y. W., KOH, Y. K., KIM, M. Microstrip array antennas packaged in metal fixture for 150 GHz beamforming applications. IEEE Antennas and Wireless Propagation Letters, 2024, vol. 23, no. 3, p. 1055–1059. DOI: 10.1109/LAWP.2023.3343698
  4. ZHANG, J. R., ZHENG, S. Y., YANG, N. An efficient broadband symmetrical Doherty power amplifier with extended back-off range. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 4, p. 1316–1320. DOI: 10.1109/TCSII.2022.3227045
  5. CHI, P., YANG, T. Novel 1.5-1.9 GHz tunable single-to-balanced bandpass filter with constant bandwidth. IEEE Microwave and Wireless Components Letters, 2016, vol. 26, no. 12, p. 972–974. DOI: 10.1109/LMWC.2016.2623251
  6. LI, H.-Y., XU, J.-X., ZHANG, X. Y. Single-to-balanced and balanced-to-balanced dual-channel filters using multilayer substrate integrated waveguide cavities. IEEE Transactions on Industrial Electronics, 2021, vol. 68, no. 3, p. 2389–2399. DOI: 10.1109/TIE.2020.2975490
  7. ZHU, X., YANG, T., CHI, P.-L., et al. Novel tunable isolation network used in ring-type single-to-balanced, power-dividing, and single-ended filter with arbitrary power-division ratios. IEEE Transactions on Microwave Theory and Techniques, 2020, vol. 68, no. 2, p. 666–680. DOI: 10.1109/TMTT.2019.2949787
  8. YADAV, A. N., BHATTACHARJEE, R. Gysel type unbalanced to-balanced equal power divider. In The 47th European Microwave Conference (EuMC). Nuremberg (Germany), 2017, p. 1–4. DOI: 10.23919/EuMC.2017.8230784
  9. ZHANG, W., LIU, Y., WU, Y., et al. Novel planar compact coupled-line single-ended-to-balanced power divider. IEEE Transactions on Microwave Theory and Techniques, 2017, vol. 65, no. 8, p. 2953–2963. DOI: 10.1109/TMTT.2017.2685389
  10. XIA, Z., WANG, J., LU, Q.-Y., et al. Development of an unbalanced-to-balanced filtering power divider with sequential rotation phase characteristic. IEEE Microwave and Wireless Technology Letters, 2024, vol. 34, no. 1, p. 21–24. DOI: 10.1109/LMWT.2023.3335272
  11. CHEN, S., YU, Y., TANG, M. Planar out-of-phase Gysel power divider with high power splitting ratio. Electronics Letters, 2015, vol. 51, no. 24, p. 2010–2012. DOI: 10.1049/el.2015.1956
  12. XIA, B., CHENG, J., XIONG, C., et al. A novel design of compact out-of-phase power divider with arbitrary ratio. IEEE Transactions on Microwave Theory and Techniques, 2020, vol. 68, no. 12, p. 5235–5243. DOI: 10.1109/TMTT.2020.3028085
  13. ZHAO, X., HUANG, Q., GAN, J., et al. A planar out-of-phase Gysel power divider with high power division. In The 6th International Conference on Communication and Information Systems (ICCIS). Chongqing (China), 2022, p. 17–21. DOI: 10.1109/ICCIS56375.2022.9998135
  14. YADAV, A. N., BHATTACHARJEE, R. Unbalanced-to-balanced power divider with arbitrary power division. Progress In Electromagnetics Research C, 2017, vol. 76, p. 43–54. DOI: 10.2528/PIERC17061503
  15. LI, L., MAO, J.-F., WU, L.-S. A single-ended-to-balanced impedance-transforming branch-line coupler with arbitrary power division ratio. IEEE Transactions on Microwave Theory and Techniques, 2019, vol. 67, no. 3, p. 949–956. DOI: 10.1109/TMTT.2019.2892433
  16. WANG, W., ZHENG, Y., WU, Y. Miniaturized single-ended-to balanced arbitrary four-section coupled-line coupler with inherent impedance matching. IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, vol. 67, no. 10, p. 1929–1933. DOI: 10.1109/TCSII.2019.2960870
  17. GOMEZ-GARCIA, R., MUÑOZ-FERRERAS, J., PSYCHOGIOU, D. RF reflectionless filtering power dividers. IEEE Transactions on Circuits and Systems II: Express Briefs, 2019, vol. 66, no. 6, p. 933 to 937. DOI: 10.1109/TCSII.2018.2875172
  18. ZHU, Z., WANG, Z., BAI, Y., et al. High-selectivity reflectionless unbalanced-to-balanced filtering power combiner. Radioengineering, 2021, vol. 30, no. 3, p. 517–523. DOI: 10.13164/RE.2021.0517
  19. LI, S., WANG, X., WANG, J., et al. Design of compact singleended-to-balanced filtering power divider with wideband common mode suppression. Electronics Letters, 2019, vol. 55, no. 17, p. 947 to 949. DOI: 10.1049/el.2019.1919
  20. ZHU, H., QIN, P. Y., GUO, Y. J. Single-ended-to-balanced power divider with extended common-mode suppression and its application to differential 2×4 Butler matrices. IEEE Transactions on Microwave Theory and Techniques, 2020, vol. 68, no. 4, p. 1510 to 1519. DOI: 10.1109/TMTT.2019.2963658
  21. ZHU, Y., SONG, K., FAN, M., et al. Wideband single-ended-to balanced power divider with intrinsic common-mode suppression. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 4, p. 379–382. DOI: 10.1109/LMWC.2020.2973863
  22. LIU, W.-S., XU, L., WEI, F., et al. Miniaturized single-ended-to balanced filtering power divider with high selectivity based on tri mode resonator. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 11, p. 4048–4052. DOI: 10.1109/TCSII.2023.3281510
  23. JONES, E. M. T. Coupled-strip-transmission-line filters and directional couplers. IRE Transactions on Microwave Theory and Techniques, 1956, vol. 4,
  24. no. 2, p. 75–81. DOI: 10.1109/TMTT.1956.1125022
  25. EISENSTADT, W. R., STENGEL, B., THOMPSON, B. M. Microwave Differential Circuit Design Using Mixed-Mode S Parameters. Boston (USA): Artech House, 2006. ISBN: 9781580539333
  26. Product parameter of the microwave substrate. [Online] Cited 202408-24. Available at: http://www.wang-ling.com.cn/product/127.html
  27. MABROUK, M., BOUSBIA, L. Study and design of RF dual-band bandpass filter, validation and confirmation of experimental measurements. Circuits and Systems, 2011, vol. 2, no. 4, p. 293 to 296. DOI: 10.4236/cs.2011.24041

Keywords: SETB power divider, enhanced input-matching bandwidth, out-of-phase, coupled line

A. Panajotovic, J. Anastasov, N. Sekulovic, D. Milic, D. Milovic, N. Milosevic [references] [full-text] [DOI: 10.13164/re.2024.0619] [Download Citations]
Sum Rate Analysis of Downlink NOMA over alpha-F Fading Channels

In this paper, our focus is towards resource allocation in a multiuser downlink non-orthogonal multiple access (NOMA) system. In the power-domain NOMA, where multiple access is realized by assigning different power levels to the clustered users, a certain degree of advantage of NOMA depends on clustering of users and power levels allocated to them. This study proposes a new power allocation algorithm, based on sum rate as performance criterion, which is applied for the clusters defined by High-High/High-Low pairing scheme. The proposed algorithm takes into account fairness between clustered users from the acquired users’ rate point of view. It provides better sum rate performance of NOMA compared to OMA, but also a low gap between the individual rates of paired users. The detailed numerical and independent simulation results for the downlink NOMA over general alpha-F fading channels are shown.

  1. YANG, P., XIAO, Y., XIAO, M., et al. 6G wireless communications: Vision and potential techniques. IEEE Network, 2019, vol. 33, no. 4, p. 70–75. DOI: 10.1109/MNET.2019.1800418
  2. SAITO, Y., KISHIYAMA, Y., BENJEBBOUR, A., et al. Nonorthogonal multiple access (NOMA) for cellular future radio access. In Proceedings of the 77th IEEE Vehicular Technology Conference (VTC Spring). Dresden (Germany), 2013, p. 1–5. DOI: 10.1109/VTCSpring.2013.6692652
  3. DAI, L., WANG, B., YUAN, Y., et al. Non-orthogonal multiple access for 5G: Solutions, challenges, opportunities, and research trends. IEEE Communications Magazine, 2015, vol. 53, no. 9, p. 74–81. DOI: 10.1109/MCOM.2015.7263349
  4. GHOUS, M., HASSAN, A. K., ABBAS, Z. H., et al. Cooperative power-domain NOMA systems: An overview. Sensors, 2022, vol. 22, no. 24, p. 1–39. DOI: 10.3390/s22249652
  5. QUAN, C., YADAV, A., GENG, B., et al. A novel spectrally efficient uplink hybrid-domain NOMA system. IEEE Communications Letters, 2020, vol. 24, no. 11, p. 2609–2613. DOI: 10.1109/LCOMM.2020.3011030
  6. ZHAO, J., HUO, M., KANG, S., et al. Exploiting hybrid nonorthogonal multiple access in satellite-terrestrial systems with randomly deployed users. Electronics Letters, 2022, vol. 58, no. 25, p. 1022–1025. DOI: 10.1049/ell2.12679
  7. LI, G., MISHRA, D. Cooperative NOMA networks: User cooperation or relay cooperation? In 2020 IEEE International Conference on Communications (ICC). Dublin (Ireland), 2020, p. 1–6. DOI: 10.1109/icc40277.2020.9148973
  8. HUANG, Y., ZHANG, C., WANG, J., et al. Signal processing for MIMO-NOMA: Present and future challenges. IEEE Wireless Communications, 2018, vol. 25, no. 2, p. 32–38. DOI: 10.1109/MWC.2018.1700108
  9. ZENG, M., YADAV, A., DOBRE, O. A., et al. Capacity comparison between MIMO-NOMA and MIMO-OMA with multiple users in a cluster. IEEE Journal on Selected Areas in Communications, 2017, vol. 35, no. 10, p. 2413–2424. DOI: 10.1109/JSAC.2017.2725879
  10. LIU, C. H., LIANG, D. C. Heterogeneous networks with power allocation analysis. IEEE Transactions on Wireless Communications, 2018, vol. 17, no. 5, p. 3524–3539. DOI: 10.1109/TWC.2018.2816923
  11. MOHSAN, S. A. H., SADIQ, M., LI, Y., et al. NOMA-based VLC systems: A comprehensive review. Sensors, 2023, vol. 23, no. 6, p. 1–30. DOI: 10.3390/s23062960
  12. AKBAR, A., JANGSHER, S., BHATTI, F. A. NOMA and 5G emerging technologies: A survey on issues and solution techniques. Computer Networks, 2021, vol. 190, p. 1–40. DOI: 10.1016/j.comnet.2021.107950
  13. SHAHAB, M. B., IRFAN, M., KADER, M. F., et al. User pairing schemes for capacity maximization in non-orthogonal multiple access systems. Wireless Communications and Mobile Computing, 2016, vol. 16, p. 2884–2894. DOI: 10.1002/wcm.2736
  14. ISLAM, S. M. R., ZENG, M., DOBRE, O. A., et al. Resource allocation for downlink NOMA systems: Key techniques and open issues. IEEE Wireless Communications, 2018, vol. 25, no. 2, p. 40–47. DOI: 10.1109/MWC.2018.1700099
  15. RAUNIYAR, A., ENGELSTAD, P., ØSTERBØ, O. N. An adaptive user pairing strategy for uplink non-orthogonal multiple access. In Proceedings of the 31st IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications. London (UK), 2020, p. 1–7. DOI: 10.1109/PIMRC48278.2020.9217383
  16. JUN, L., GAO, T., HE, B., et al. Power allocation and user grouping for NOMA downlink systems. Applied Sciences, 2023, vol. 13, no. 4, p. 1–13. DOI: 10.3390/app13042452
  17. TAI, T. V., XUAN UYEN, N. T., KHOA, D. L. Optimal user clustering and power allocation in NOMA systems. In Proceedings of 2022 International Conference on Advanced Technologies for Communications (ATC). Ha Noi (Vietnam), 2022, p. 350–355. DOI: 10.1109/ATC55345.2022.9943029
  18. MOUNI, N. S., PAVAN REDDY, M., KUMAR, A., et al. Adaptive multi-user clustering and power allocation for NOMA systems with imperfect SIC. In Proceedings of the 16th International Conference on Communication Systems & Networks (COMSNETS). Bengaluru (India), 2024, p. 747–751. DOI: 10.1109/COMSNETS59351.2024.10426961
  19. SANTOS, Y. P., SILVEIRA, L. F. Q. Adaptive clustering of users in power domain NOMA. Sensors, 2023, vol. 23, no. 11, p. 1–16. DOI: 10.3390/s23115314
  20. KUMARESAN, S. P., TAN, C. K., NG, Y. H. Efficient user clustering using a low-complexity artificial neural network (ANN) for 5G NOMA systems. IEEE Access, 2020, vol. 8, no. 2, p. 179307–179316. DOI: 10.1109/ACCESS.2020.3027777
  21. HAMEDOON, S. M., CHATTHA, J. N., BILAL, M. Towards intelligent user clustering techniques for non-orthogonal multiple access: A survey. EURASIP Journal on Wireless Communications and Networking, 2024, vol. 7, no. 1, p. 1–33. DOI: 10.1049/s13638-024-02333-z
  22. BENJEBBOVU, A., LI, A., SAITO, Y., et al. System-level performance of downlink NOMA for future LTE enhancements. In Proceedings of IEE Globecom Workshops (GC Wkshps). Atlanta (GA, USA), 2013, p. 66–70. DOI: 10.1109/GLOCOMW.2013.6824963
  23. MANGLAYEV, T., KIZILIRMAK, R. C., KHO, Y. H., et al. AI based power allocation for NOMA. Wireless Personal Communications, 2022, vol. 124, no. 4, p. 3253–3261. DOI: 10.1007/s11277-022-09511-6
  24. HOU, T., SUN, X., SONG, Z. Outage performance for nonorthogonal multiple access with fixed power allocation over Nakagami-m fading channels. IEEE Communications Letters, 2018, vol. 22, no. 4, p. 744–747. DOI: 10.1109/LCOMM.2018.2799609
  25. SRINIVASARAO, K., SURENDAR, M. Outage analysis of downlink non-orthogonal multiple access scheme over Rician fading channel. In Proceedings of the 4th IEEE Conference on Information & Communication Technology (CICT). Chennai (India), 2020, p. 1–5. DOI: 10.1109/CICT51604.2020.9312114
  26. ALQAHTANI, A. S., ALSUSA, E. Performance analysis of downlink NOMA system over α-η-µ generalized fading channel. In Proceedings of the 91st IEEE Vehicular Technology Conference (VTC2020-Spring). Antwerp (Belgium), 2020, p. 1–5. DOI: 10.1109/VTC2020-SPRING48590.2020.9128419
  27. ALQAHTANI, A., ALSUSA, E., AL-DWEIK, A., et al. Performance analysis for downlink NOMA over α-µ generalized fading channels. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 7, p. 6814–6825. DOI: 10.1109/TVT.2021.3082917
  28. ASLAN, K., GUCLUOGLU, T. Performance analysis of downlink-NOMA over generalized-K fading channels. Physical Communication, 2021, vol. 48, p. 1–11. DOI: 10.1016/j.phycom.2021.101414
  29. ELHALAWANY, B. M., JAMEEL, F., DA COSTA, D. B., et al. Performance analysis of downlink NOMA systems over k-µ shadowed fading channels. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 1, p. 1046–1050. DOI: 10.1109/TVT.2019.2953109
  30. ABUALHAOL, Y. I., MATALGAH, M. M. Performance analysis of cooperative multi-carrier relay-based UAV networks over generalized fading channels.
  31. International Journal of Communication Systems, 2011, vol. 24, no. 8, p. 1049–1064. DOI: 10.1002/dac.1212
  32. RASHEED, H., HAROON, F., RAJATHEVA, N. Energy detection with diversity combining over KG fading for cognitive VANET. In Proceedings of the 84th IEEE Vehicular Technology Conference (VTC-Fall). Montreal (QC, Canada), 2016, p. 1–5. DOI: 10.1109/VTCFall.2016.7880983
  33. BADARNEH, O. S. The α-F composite fading distribution: Statistical characterization and applications. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 8, p. 8097–8106. DOI: 10.1109/TVT.2020.2995665
  34. ANSARI, R. I., CHRYSOSTOMOU, C., HASSA, S. A., et al. 5G D2D networks: Techniques, challenges, and future prospects. IEEE System Journal, 2018, vol. 12, no. 4, p. 3970–3984. DOI: 10.1109/JSYST.2017.2773633
  35. PANAJOTOVIC, A., ANASTASOV, J., MILIC, D., et al. On the performance analysis for paired users NOMA downlink over α-F fading channels. China Communications, 2024, p. 1–12.
  36. LE, N. P., TRAN, L. C., HUANG, X., et al. Performance analysis of uplink NOMA systems with hardware impairments and delay constraints over composite fading channels. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 7, p. 6881–6897. DOI: 10.1109/TVT.2021.3086045
  37. ISLAM, S. M. R., ZENG, M., DOBRE, O. A., et al. Nonorthogonal multiple access (NOMA): How it meets 5G and beyond. DOI: 10.48550/arXiv.1907.10001
  38. ALDABABSA, M., TOKA, M., GOKҀELI, S., et al. A tutorial on non-orthogonal multiple access for 5G and beyond. Wireless Communications and Mobile Computing, 2018, p. 1–24. DOI: 10.1155/2018/9713450
  39. DOGRA, T., BHARTU, M. R. User pairing and power allocation strategies for downlink NOMA-based VLC systems: An overview. AEU-International Journal of Electronics and Communications, 2022, vol. 149, p. 1–17. DOI: 10.1016/j.aeue.2022.154184
  40. ALI, M. S., TABASSUM, H., HOSSAIN, E. Dynamic user clustering and power allocation for uplink and downlink nonorthogonal multiple access (NOMA) systems. IEEE Access, 2016, vol. 4, p. 6325–6343. DOI: 10.1109/ACCESS.2016.2604821
  41. OVIEDO, J. A., SADJADPOUR, H. R. A new NOMA approach for fair power allocation. In Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). San Francisco (CA, USA), 2016, p. 843–847. DOI: 10.1109/INFCOMW.2016.7562194
  42. AL-ABBASI, Z. Q., SO, D. K. C. User-pairing based nonorthogonal multiple access (NOMA). In Proceedings of the 83rd IEEE Vehicular Technology Conference (VTC Spring). Nanjing (China), 2016, p. 1–5. DOI: 10.1109/VTCSpring.2016.7504524
  43. CHEN, Z., DING, Z., DAI, X., et al. An optimization perspective of the superiority of NOMA compared to conventional OMA. IEEE Transactions on Signal Processing, 2017, vol. 65, no. 19, p. 5191–5202. DOI: 10.1109/TSP.2017.2725223
  44. SREENU, S., KALPANA, N. Innovative power allocation strategy for NOMA systems by employing the modified ABC algorithm. Radioengineering, 2022, vol. 31, no. 3, p. 312–322. DOI: 10.13164/re.2022.0312

Keywords: Non-orthogonal Multiple Access (NOMA), composite alpha-F fading, user pairing, resource power allocation, downlink NOMA, rate fairness

S. L. Ajeel, R. T. Hammed [references] [full-text] [DOI: 10.13164/re.2024.0629] [Download Citations]
Multilayered Stub Loaded-SIR for Compact Dual-BPF and Quad-channel Diplexer Design

This paper considers a novel design technique of compact dual-BPF and four-channel diplexer for a multi-service communication system. The suggested dual-band passband filter is constructed by a double-layered stub loaded-stepped impedance resonator (SL-SIR), leading to a tiny circuit area, and lightweight, low cost, and good characteristic performance. Herein, the SL-SIR resonant odd-mode is used to realize the first passband, while the resonant even-mode is used to realize the second passband. Also, the proposed three-port quad-channel diplexer is performed by two different double-layered dual-passband filters, which also have a very compact circuit area. For practical verification, a two-passband filter working at 2.5/4 GHz with a circuit area of 91.7〖mm〗^2 and a four-channel diplexer working at 2.5/4 GHz and 3.5/5.2 GHz with a circuit area of 0.0639 〖λ_g〗^2 excluding feeding ports are designed, manufactured, and measured. The electromagnetic simulated and measured responses are compared and discussed. Obviously, the comparison show good agreement improving the expected filtering response. The diplexer offers insertion/return losses of about (0.44/0.74) dB/(0.45/1.12) dB for channel 1/channel 2, and (21.30/21.72) dB/(19.81/20.54) dB for channel 3/channel 4.

  1. HAMMED, R. T. Compact Marchand balun circuit for UWB application. AEU-International Journal of Electronics and Communication, 2015, vol. 69, no. 5, p. 851–855. DOI: 10.1016/j.aeue.2015.02.005
  2. CHOUDHARY, D. K., CHAUDHARY, R. K. Compact lowpass and dual-band bandpass filter with controllable transmission zero/center frequencies/passband bandwidth. IEEE Transactions on Circuits and Systems: II Express Briefs, 2020, vol. 67, no. 6, p. 1044–1048. DOI: 10.1109/TCSII.2019.2931446
  3. HAMMED, R. T. Miniaturized high-order ultra-wideband bandpass filters with multiple band rejection notches. Electromagnetics, 2015, vol. 35, no. 8, p. 538–549. DOI: 10.1080/02726343.2015.1101808
  4. TAN, Z., LU, Q.-Y., CHEN, J.-X. Differential dual-band filter using ground bar-loaded dielectric strip resonators. IEEE Microwave and Wireless Components Letters, 2019, vol. 30, no. 2, p. 148–151. DOI: 10.1109/LMWC.2019.2957980
  5. HAMMED, R. T., ABDUL-JABBAR, Z. M. Multilayered stepped impedance loaded-resonator for compact dual-band rejection filter design. Electromagnetics, 2017, vol. 37, no. 8, p. 493–499. DOI: 10.1080/02726343.2017.1392717
  6. DAO, X. P., NGUYEN, V. S., TRAN, V. D., et al. A compact dual bandpass filter using dual composite right-/left-handed and open-loop ring resonators for 4G and 5G applications. Radioengineering, 2024, vol. 33, no. 1, p. 213–221. DOI: 10.13164/re.2024.0213
  7. HAMMED, R. T., ABBAS, S. M. A small dual narrowband BPF with ultra-rejection band using grounded stepped-impedance resonator. IETE Journal of Research, 2023, vol. 69, no. 5, p. 2811–2816. DOI: 10.1080/03772063.2021.1906337
  8. LIANG, G.-Z., CHEN, F.-C. A compact dual-wideband bandpass filter based on open-/short-circuited stubs. IEEE Access, 2020, vol. 8, p. 20488–20492.
  9. DOI: 10.1109/ACCESS.2020.2968518
  10. CHANG, H., SHENG, W., CUI, J., et al. Multilayer dual-band bandpass filter with multiple transmission zeros using discriminating coupling. IEEE Microwave and Wireless Components Letters, 2020, vol. 30, no. 7, p. 645–648. DOI: 10.1109/LMWC.2020.2995181
  11. LI, D., WANG, J., LIU, Y., et al. Selectivity‐enhancement technique for parallel‐coupled SIR based dual‐band bandpass filter. Microwave and Optical Technology Letters, 2021, vol. 63, no. 3, p. 787–792. DOI: 10.1002/mop.32672
  12. KISHORE, S., ARORA, A, PHANI KUMAR, K. V., et al. Compact dual‐band bandpass filter with high‐passband isolation using coupled lines and open stub. IEEE Microwave and Wireless Components Letters, 2021, vol. 63, no. 11, p. 2710 to 2714. DOI: 10.1002/mop.32940
  13. KARIMI, G., AMIRIAN, M., LALBAKHSH, A., et al. A new microstrip coupling system for realization of a differential dual band bandpass filter. AEU-International Journal of Electronics and Communications, 2019, vol. 99, p. 186–192. DOI: 10.1016/j.aeue.2018.11.004
  14. SHAHEEN, M. M., MAHMOUD, N. M., ALI, M. A., et al. Implementation of a highly selective microstrip diplexer with low insertion loss using square open-loop resonators and a T-junction combiner. Radioengineering, 2022, vol. 31, no. 3, p. 357–361. DOI: 10.13164/re.2022.0357
  15. YANG, L., GOMEZ-GARCIA, R. Multilayer microstrip closely spaced-channel wideband diplexer with highly selective fourth order filtering responses. IEEE Transactions on Circuits and Systems: II Express Briefs, 2022, vol. 69, no. 9, p. 3769–3773. DOI: 10.1109/TCSII.2022.3173910
  16. NWAYANA, A. O., YEO, K. S. K. Microwave diplexer purely based on direct synchronous and asynchronous coupling. Radioengineering, 2016, vol. 25, no. 2, p. 247–252. DOI: 10.13164/re.2016.0247
  17. HAMMED, R. T. Multilayered U-shape diplexer for high performance multifunctional wireless communication systems. AEU-International Journal of Electronics and Communications, 2022, vol. 150, p. 1–5. DOI: 10.1016/j.aeue.2022.154217
  18. CHEN, C.-F., LIN, C.-Y., TSENG, B.-H., et al. A compact microstrip quad-channel diplexer with high-selectivity and high isolation performances. In IEEE MTT-S International Microwave Symposium (IMS2014). Tampa (FL, USA), 2014, p. 1–3. DOI: 10.1109/MWSYM.2014.6848271
  19. YAHYA, S. I., NOURI, L. A low-loss four-channel microstrip diplexer for wideband multi-service wireless applications. AEU International Journal of Electronics and Communications, 2021, vol. 133, p. 1–7. DOI: 10.1016/j.aeue.2021.153670
  20. CHEN, C. F., WANG, G. Y., LI, J. J. Compact microstrip dual band bandpass filter and quad-channel diplexer based on quint mode stub-loaded resonators. IET Microwaves, Antennas and Propagation, 2018, vol. 12, no. 12, p. 1913–1919. DOI: 10.1049/iet-map.2018.0079
  21. ZHANG, J., PANG, D., WANG, W., et al. Microstrip quad channel diplexer using quad‐mode square ring resonators. IEEE Microwave and Wireless Components Letters, 2019, vol. 61, no. 8, p. 2003–2007. DOI: 10.1002/mop.31827
  22. DEL RIO, J. L M., FERNANDEZ-PRIETO, A., MARTEL, J., et al. Compact multilayered balanced-to-balanced dual-and triband diplexers based on magnetically coupled open-loop resonators. IEEE Access, 2022, vol. 10, p. 125435–125444. DOI: 10.1109/ACCESS.2022.3225542
  23. MAKIMOTO, M., YAMASHITA, S. Microwave Resonators and Filters for Wireless Communication: Theory, Design and Application. Berlin (Germany): Springer, 2010. DOI: 10.1007/978-3-662-04325-7
  24. AGILENT TECHNOLOGIES. Advanced Design System (ADS). Palo Alto (CA, USA), 2016.
  25. HAMMED, R. T. Miniaturized dual-band bandpass filter using E-shape microstrip structure. AEU-International Journal of Electronics and Communications, 2015, vol. 69, no. 11, p. 1667 to 1671. DOI: 10.1016/j.aeue.2015.08.003

Keywords: Stub loaded-stepped impedance resonator, double-layered dual-BPF, four-channel diplexer, compact multi-channel diplexer, multilayered dual-PBF

W. Chen, J. Yu, Z. Yang, X. Yang [references] [full-text] [DOI: 10.13164/re.2024.0636] [Download Citations]
Design of Wideband Dual-band Substrate Integrated Waveguide Slot Antenna

A dual-band Half Mode Substrate Integrated Waveguide (HMSIW) slot antenna operating at 3.5 GHz and 4.9 GHz is proposed. The two operational bands of the proposed antenna are achieved by combining two HMSIW slot antennas. The -10 dB impedance bandwidth and realized gain of the antenna at 3.5 GHz and 4.9 GHz are 3.47-3.68 GHz with a gain of 3.9 dBi and 4.62-5.16 GHz with a gain of 5.5 dBi, respectively. Compared with the traditional SIW dual-band antenna, the bandwidth of the antenna has been significantly improved. The proposed antenna has a simple structure and exhibits excellent radiation performance in both operating frequency bands, making it suitable for 5G mobile communication systems.

  1. GONG, K., HONG, W., ZHANG, Y., et al. Substrate integrated waveguide quasi-elliptic filters with controllable electric and magnetic mixed coupling. IEEE Transactions on Microwave Theory and Techniques, 2012, vol. 60, no. 10, p. 3071–3078. DOI: 10.1109/TMTT.2012.2209437
  2. CHEN, K., LEE, J., CHAPPELL, W. J., et al. Co-design of highly efficient power amplifier and high-Q output bandpass filter. IEEE Transactions on Microwave Theory and Techniques, 2013, vol. 61, no. 11, p. 3940–3950. DOI: 10.1109/TMTT.2013.2284485
  3. BELENGUER, A., ESTEBAN, H., BORIA, V. E. Novel empty substrate integrated waveguide for high-performance microwave integrated circuits. IEEE Transactions on Microwave Theory and Techniques, 2014, vol. 62, no. 4, p. 832–839. DOI: 10.1109/TMTT.2014.2309637
  4. GAO, Y., ZHANG, F., LV, X., et al. Substrate integrated waveguide filter–amplifier design using active coupling matrix technique. IEEE Transactions on Microwave Theory and Techniques, 2020, vol. 68, no. 5, p. 1706–1716. DOI: 10.1109/TMTT.2020.2972390
  5. SONG, K., FAN, Y., ZHANG, Y. Eight-way substrate integrated waveguide power divider with low insertion loss. IEEE Transactions on Microwave Theory and Techniques, 2008, vol. 56, no. 6, p. 1473–1477. DOI: 10.1109/TMTT.2008.923897
  6. KHAN, A. A., MANDAL, M. K. Miniaturized substrate integrated waveguide (SIW) power dividers. IEEE Microwave and Wireless Components Letters, 2016, vol. 26, no. 11, p. 888–890. DOI: 10.1109/LMWC.2016.2615005
  7. CHEN, H., CHE, W., WANG, X., et al. Size-reduced planar and nonplanar SIW Gysel power divider based on low temperature cofired ceramic technology. IEEE Microwave and Wireless Components Letters, 2017, vol. 27, no. 12, p. 1065–1067. DOI: 10.1109/LMWC.2017.2765558
  8. MUKHERJEE, S., BISWAS, A., SRIVASTAVA, K. V. Broadband substrate integrated waveguide cavity-backed bow-tie slot antenna. IEEE Antennas and Wireless Propagation Letters, 2014, vol. 13, p. 1152–1155. DOI: 10.1109/LAWP.2014.2330743
  9. YUN, S., KIM, D. Y., NAM, S. Bandwidth enhancement of cavity backed slot antenna using a via-hole above the slot. IEEE Antennas and Wireless Propagation Letters, 2012, vol. 11, p. 1092–1095. DOI: 10.1109/LAWP.2012.2215911
  10. ASTUTI, D. W., ASVIAL, M., ZULKIFLI, F. Y., et al. Bandwidth enhancement on half-mode substrate integrated waveguide antenna using cavity-backed triangular slot. International Journal of Antennas and Propagation, 2020, vol. 2020, p. 1–9. DOI: 10.1155/2020/1212894
  11. SHI, Y., LIU, J., LONG, Y. Wideband triple- and quad-resonance substrate integrated waveguide cavity-backed slot antennas with shorting vias. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 11, p. 5768–5775. DOI: 10.1109/TAP.2017.2755118
  12. DECKMYN, T., AGNEESSENS, S., RENIERS, A. F., et al. A novel 60 GHz wideband coupled half-mode/quarter-mode substrate integrated waveguide antenna. IEEE Transactions on Antennas and Propagation, 2017, vol. 65, no. 12, p. 6915–6926. DOI: 10.1109/TAP.2017.2760360
  13. GENG, Y., WANG, J., LI, Y., et al. A Ka-band leaky-wave antenna array with stable gains based on HMSIW structure. IEEE Antennas and Wireless Propagation Letters, 2022, vol. 21, no. 8, p. 1597 to 1601. DOI: 10.1109/LAWP.2022.3174957
  14. PRADHAN, N. C., SUBRAMANIAN, K. S., BARIK, R. K., et al. Shielded HMSIW-based self-triplexing antenna with high isolation for WiFi/WLAN/ISM band. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 6, p. 1941–1945. DOI: 10.1109/TCSII.2022.3231829
  15. MUKHERJEE, S., BISWAS, A., SRIVASTAVA, K. V. Substrate integrated waveguide cavity-backed dumbbell-shaped slot antenna for dual-frequency applications. IEEE Antennas and Wireless Propagation Letters, 2015, vol. 14, p. 1314–1317. DOI: 10.1109/LAWP.2014.2384018
  16. JI, S., DONG, Y., WEN, S., et al. C/X dual-band circularly polarized shared-aperture antenna. IEEE Antennas and Wireless Propagation Letters, 2021, vol. 20, no. 12, p. 2334–2338. DOI: 10.1109/LAWP.2021.3110529
  17. LIU, C. M., XIAO, S. Q., WU, K. Miniaturized cylindrical open ended SIW cavity antenna and its dual-band applications. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 8, p. 4390–4400. DOI: 10.1109/TAP.2020.3048553
  18. LI, L., WU, S., PANG, D., et al. A fifth-order single-layer dual-band half-mode SIW filtering antenna with a multifunctional single slot. IEEE Antennas and Wireless Propagation Letters, 2021, vol. 20, no. 9, p. 1676–1680. DOI: 10.1109/LAWP.2021.3093062
  19. XU, F., WU, K. Guided-wave and leakage characteristics of substrate integrated waveguide. IEEE Transactions on Microwave Theory and Techniques, 2005, vol. 53, no. 1, p. 66–73. DOI: 10.1109/TMTT.2004.839303
  20. HONG, W., LIU, B., WANG, Y., et al. Half mode substrate integrated waveguide: a new guided wave structure for microwave and millimeter wave application. In 2006 Joint 31st International Conference on Infrared Millimeter Waves and 14th International Conference on Terahertz Electronics. Shanghai (China), 2006, p. 219–219. DOI: 10.1109/ICIMW.2006.368427

Keywords: Half Mode Substrate Integrated Waveguide (HMSIW), antenna, antenna miniaturization, cavity-backed slot antenna

L. L. Zhang，X. J. Wang，J. H. Liu, Q. Z. Fang [references] [full-text] [DOI: 10.13164/re.2024.0642] [Download Citations]
A Low-Complexity Transformer-CNN Hybrid Model Combining Dynamic Attention for Remote Sensing Image Compression

Deep learning-based methods have recently made enormous progress in remote sensing image compression. However, conventional CNN is complex to adaptively capture important information from different image regions. In addition, previous transformer-based compression methods have introduced high computational complexity to the models. Remote sensing images contain rich spatial and channel information. The effective extraction of these two kinds of information for image compression remains challenging. To address these issues, we propose a new low-complexity end-to-end image compression framework combining CNN and transformer. This framework includes two critical modules: the Dynamic Attention Model (DAM) and the Hyper-Prior Hybrid Attention Model (HPHAM). By employing dynamic convolution as the core part of the DAM, the DAM can dynamically adjust the attention weights according to the image content. HPHAM effectively integrates non-local and channel information of latent representations through the parallel running of Gated Channel Attention (GCA) and multi-head self-attention. Experiments demonstrate that the proposed approach outperforms existing mainstream deep-learning image compression approaches and conventional image compression methods, achieving optimal rate-distortion performance on three datasets. Code is available at https://github.com/jiahuiLiu11/LTCHM.

  1. WALLACE, G. K. The JPEG still picture compression standard. Communications of the ACM, 1991, vol. 34, no. 4, p. 30–44. DOI: 10.1145/103085.103089
  2. RABBANI, M., JOSHI, R. An overview of the JPEG2000 still image compression standard. Signal Processing: Image Communication, 2002, vol. 17, no. 1, p. 3–48. DOI: 10.1016/S0923-5965(01)00024-8
  3. SULLIVAN, G. J., OHM, J. R., HAN, W. J., et al. Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on Circuits and Systems for Video Technology, 2012, vol. 22, no. 12, p. 1649–1668. DOI: 10.1109/TCSVT.2012.2221191
  4. GARCIA-VILCHEZ, F., SERRA-SAGRISTA, J. Extending the CCSDS recommendation for image data compression for remote sensing scenarios. IEEE Transactions on Geoscience and Remote Sensing, 2009, vol. 47, no. 10, p. 3431–3445. DOI: 10.1109/TGRS.2009.2021067
  5. TANG, Z., WANG, H., YI, X., et al. Joint graph attention and asymmetric convolutional neural network for deep image compression. IEEE Transactions on Circuits and Systems for Video Technology, 2022, vol. 33, no. 1, p. 421–433. DOI: 10.1109/TCSVT.2022.3199472
  6. AFJAL, M. I., UDDIN, P., MAMUN, A., et al. An efficient lossless compression technique for remote sensing images using segmentation based band reordering heuristics. International Journal of Remote Sensing, 2021, vol. 42, no. 2, p. 756–781. DOI: 10.1080/01431161.2020.1812130
  7. HONG, G., HALL, G., TERRELL, T. Discrete cosine transform data compression applied to satellite sensor images. Remote Sensing, 1995, vol. 16, no. 5, p. 835–850. DOI: 10.1080/01431169508954447
  8. ZHANG, L., QIU, B. Fast orientation prediction-based discrete wavelet transform for remote sensing image compression. Remote Sensing Letters, 2013, vol. 4, no. 12, p. 1156–1165. DOI: 10.1080/2150704X.2013.858838
  9. XIANG, X., JIANG, Y., SHI, B. Hyper-spectral image compression based on band selection and slant Haar type orthogonal transform. International Journal of Remote Sensing, 2024, vol. 45, no. 5, p. 1658–1677. DOI: 10.1080/01431161.2024.2318775
  10. SHI, C., ZHANG, J., ZHANG, Y. A novel vision-based adaptive scanning for the compression of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2015, vol. 54, no. 3, p. 1336–1348. DOI: 10.1109/TGRS.2015.2478145
  11. BALLE, J., LAPARRA, V., SIMONCELLI, E. P. End-to-end optimized image compression. arXiv preprint, 2016, p. 1–14. DOI: 10.48550/arXiv.1611.01074
  12. BALLE, J., MINNEN, D., SINGH, S., et al. Variational image compression with a scale hyperprior. arXiv preprint, 2018, p. 1–23. DOI: 10.48550/arXiv.1802.01436
  13. MINNEN, D., BALLE, J., TODERICI, G. D. Joint autoregressive and hierarchical priors for learned image compression. Advances in Neural Information Processing Systems, 2018, vol. 31, p. 1–22. DOI: 10.48550/arXiv.1809.02736
  14. WANG, Z., SIMONCELLI, E.P., BOVIK, A.C. Multi scale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers. Pacific Grove(CA, USA), 2003, p. 1398–1402. DOI: 10.1109/ACSSC.2003.1292216
  15. HE, D., YANG, Z., PENG, W., et al. Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans (LA, USA), 2022, p. 5718–5727. DOI: 10.48550/arXiv.2203.10886
  16. XIE, Y., CHENG, K. L., CHEN, Q. Enhanced invertible encoding for learned image compression. In Proceedings of the 29th ACM International Conference on Multimedia. Virtual Event China, 2021, p. 162–170. DOI: 10.1145/3474085.3475213
  17. ZOU, R., SONG, C., ZHANG, Z. The devil is in the details: Window-based attention for image compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans (LA, USA), 2022, p. 17492–17501. DOI: 10.48550/arXiv.2203.08450
  18. QIAN, Y., TAN, Z., SUN, X., et al. Learning accurate entropy model with global reference for image compression. arXiv preprint, 2020, p. 1–13. DOI: 10.48550/arXiv.2010.08321
  19. CHEN, T., LIU, H., MA, Z., et al. End-to-end learnt image compression via non-local attention optimization and improved context modeling. IEEE Transactions on Image Processing, 2021, vol. 30, p. 3179–3191. DOI: 10.1109/TIP.2021.3058615
  20. QIAN, Y., LIN, M., SUN, X., et al. Entroformer: A transformer-based entropy model for learned image compression. arXiv preprint, 2022, p. 1–14. DOI: 10.48550/arXiv.2202.05492
  21. KOYUNCU, A. B., GAO, H., BOEV, A., et al. Contextformer: A transformer with spatio-channel attention for context modeling in learned image compression. In European Conference on Computer Vision. Tel Aviv (Israel), 2022, p. 447–463. DOI: 10.1007/978303119800726
  22. FU, C., DU, B. Remote sensing image compression based on the multiple prior information. Remote Sensing, 2023, vol. 15, no. 8, p. 1–16. DOI: 10.3390/rs15082211
  23. ZHANG, L., PAN, T., HUANG, Y., et al. SAR image compression using discretized Gaussian adaptive model and generalized subtractive normalization. IEEE Geoscience and Remote Sensing Letters, 2022, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2022.3213375
  24. ZHANG, J., ZHANG, S., WANG, H., et al. Image compression network structure based on multiscale region of interest attention network. Remote Sensing, 2023, vol. 15, no. 2, p. 1–18. DOI: 10.3390/rs15020522
  25. ZHANG, L., HU, X., PAN, T., et al. Image compression network structure based on multiscale region of interest attention network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, vol. 17, p. 138–149. DOI: 10.1109/JSTARS.2023.3326957
  26. CHENG, Z., SUN, H., TAKEUCHI, M., et al. Learned image compression with discretized gaussian mixture likelihoods and attention modules. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle (WA, USA), 2020, p. 7939–7948. DOI: 10.1109/CVPR42600.2020.00796
  27. LIN, F., SUN, H., LIU, J., et al. Multistage spatial context models for learned image compression. In IEEE International Conference on
  28. Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island (Greece), 2023, p.1–5.DOI:10.1109/ICASSP49357.2023.10095875
  29. FU, H., LIANG, F., LIN, J., et al. Learned image compression with gaussian-laplacian-logistic mixture model and concatenated residual modules. IEEE Transactions on Image Processing, 2023, vol. 32, p. 2063–2076. DOI: 10.1109/TIP.2023.3263099
  30. KINGMA, D. P., WELLING, M. Auto-encoding variational bayes. arXiv preprint, 2013, p. 1–14. DOI: 10.48550/arXiv.1312.6114
  31. DOSOVITSKIY, A., BEYER, L., KOLESNIKOV, A., et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint, 2020, p. 1–22. DOI: 10.48550/arXiv.2010.11929
  32. LIU, Z., LIN, Y., CAO, Y., et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal (QC, Canada), 2021, p. 10012–10022. DOI: 10.1109/ICCV48922.2021.00986
  33. KHOSHKHAHTINAT, A., ZAFARI, A., MEHTA, P., et al. Multi-context dual hyper-prior neural image compression. In International Conference on Machine Learning and Applications (ICMLA). Jacksonville (FL, USA), 2023, p. 618–625. DOI: 10.1109/ICMLA58977.2023.00091
  34. LIU, J., SUN, H., KATTO, J. Learned image compression with mixed transformer-CNN architectures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver (BC, Canada), 2023, p. 14388–14397. DOI: 10.1109/CVPR52729.2023.01383
  35. LOU, M., ZHOU, H. Y., YANG, S., et al. TransXNet: Learning both global and local dynamics with a dual dynamic token mixer for visual recognition. arXiv preprint, 2023, p. 1–12. DOI: 10.48550/arXiv.2310.19380
  36. NARAYANAN, M. SENetV2: Aggregated dense layer for channelwise and global representations. arXiv preprint, 2023, p. 1–9. DOI: 10.48550/arXiv.2311.10807
  37. GAO, S. H., CHENG, M. M., ZHAO, K., et al. Res2net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, vol. 43, no. 2, p. 652–662. DOI: 10.1109/TPAMI.2019.2938758
  38. KINGMA, D.P., BA, J. Adam: A method for stochastic optimization. arXiv preprint, 2014, p. 1–15. DOI: 10.48550/arXiv.1412.6980
  39. LIU, Y., YANG, W., BAI, H., et al. Region-adaptive transform with segmentation prior for image compression. arXivpreprint, 2024, p.119. DOI: 10.48550/arXiv.2403.00628
  40. BJONTEGAARD, G. Calculation of average PSNR differences between RD-curves. ITU SG16 Doc. VCEG-M33, 2001

Keywords: Remote sensing image compression, dynamic convolution, attention mechanism, gating mechanism

H. V. Nguyen, C. T. Trinh, G. H. Vu, H. V. Bui, M. D. Nguyen, S. H. Nguyen, C. D. Quach, N. T. Hoang [references] [full-text] [DOI: 10.13164/re.2024.0660] [Download Citations]
Grid-forming Control for Power Converters Based on Hybrid Energy Storage Systems During Islanding Operation

Energy storage systems are increasingly playing a pivotal role in power systems. The combination of distributed sources and energy storage systems with local loads forms an autonomous distribution grid (ADG) capable of flexibly operating in either islanding or grid-connected modes. It is essential to maintain the state variables of the grid, such as frequency and voltage, within permitted ranges during islanding mode, where the power converter control plays the most important role in the system operation. This paper proposes a control method for the power converter associated with a hybrid energy storage system (ESS) that is capable of forming the distribution grid during the islanding mode as well as following to the grid in grid-connected mode. The control method is based on a centralized energy controller to distribute energy between a battery and a supercapacitor. Accordingly, two voltage and current control loops using a PI hybrid fuzzy controller are designed. Simulation is developed using Matlab/Simulink software for a case study of a distribution network to illustrate the efficiency of the proposed control method.

  1. MESBAHI, T., RIZOUG, N., BARTHOLOMËUS, P., et al. Dynamic model of Li-ion batteries incorporating electrothermal and ageing aspects for electric vehicle applications. IEEE Transactions on Industrial Electronics, 2018, vol. 65, no. 2, p. 1298–1305. DOI: 10.1109/TIE.2017.2714118
  2. ABU-SHARKH, S., DOERFFEL, D. Rapid test and non-linear model characterization of solid-state lithium-ion batteries. Journal of Power Sources, 2004, vol. 130, no. 1–2, p. 266–274. DOI: 10.1016/j.jpowsour.2003.12.001
  3. ERAGAMREDDY, G., NAIK, S. G. Power management system for a hybrid energy storage electric vehicle using fuzzy logic controller. Bulletin of Electrical Engineering and Informatics, 2024, vol. 13, no. 3, p. 1443–1452. DOI: 10.11591/eei.v13i3.6608
  4. ALI, M., HOSSAIN, M. I., SHAFIULLAH, M. Fuzzy logic for energy management in hybrid energy storage systems integrated DC microgrid. In 2022 International Conference on Power Energy Systems and Applications (ICoPESA 2022). Nanjing (China), p. 424–429. DOI: 0.1109/ICoPESA54515.2022.9754406
  5. ARIYARATHNA, T., JAYANANDA, D., KULARATNA, N., et al. Potential of supercapacitors in novel power converters as semi ideal lossless voltage droppers. In 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON). Bejing (China), 2017, p. 1429–1434. DOI: 10.1109/IECON.2017.8216243
  6. CHOI, S. C., JEON, J. Y., YEO, T. J., et al. State-of-charge balancing control of a battery power module for a modularized battery for electric vehicle. Journal of Electrical Engineering and Technology, 2016, vol. 11, no. 3, p. 629–638. DOI: 10.5370/JEET.2016.11.3.629
  7. CASTILLO, A., GAYME, D. F. Grid-scale energy storage applications in renewable energy integration: A survey. Energy Conversion and Management, 2014, vol. 87, p. 885–894. DOI: 10.1016/j.enconman.2014.07.063
  8. AKHIL, A. A., HUFF, G., CURRIER, A. B., et al. DOE/EPRI Electricity Storage Handbook in Collaboration with NRECA. 2015, p. 1–347. [Online] Cited 2024-06-18. Available: http://www.sandia.gov/ess/publications/SAND2015-1002.pdf
  9. CAO, J., HARROLD, D., FAN, Z., et al. Deep reinforcement learning based energy storage arbitrage with accurate lithium-ion battery degradation model. IEEE Transactions on Smart Grid, 2020, vol. 11, no. 5, p. 4513–4521. DOI: 10.1109/TSG.2020.2986333
  10. PETRZELA, J. Accurate constant phase elements dedicated for audio signal processing. Applied Sciences, 2019, vol. 9, no. 22, p. 1–38. DOI: 10.3390/app9224888
  11. PETRZELA, J. Fractional-order chaotic memory with wideband constant phase elements. Entropy, 2020, vol. 22, no. 4, p. 1–32. DOI: 10.3390/E22040422
  12. HAMIDI, A., WEBER, L., NASIRI, A. EV charging station integrating renewable energy and second-life battery. In 2013 International Conference of Renewable Energy Resources and Applications (ICRERA 2013). Madrid (Spain), 2013 p. 1217–1221. DOI: 10.1109/ICRERA.2013.6749937
  13. BERTRAND, N., SABATIER, J., BRIAT, O., et al. Embedded fractional nonlinear supercapacitor model and its parametric estimation method. IEEE Transactions on Industrial Electronics, 2010, vol. 57, no. 12, p. 3991–4000. DOI: 10.1109/TIE.2010.2076307
  14. MANLA, E., MANDIC, G., NASIRI, A. Testing and modeling of lithium-ion ultracapacitors. In 2011 IEEE Energy Conversion Congress and Exposition (ECCE 2011). Phoenix (AZ, USA), 2011, p. 2957–2962. DOI: 10.1109/ECCE.2011.6064167
  15. HAZRA, P. Enhancement of inertial response of inverter based energy system and its application for dynamic performance improvement of a microgrid. M.Sc. Theses, Clemson University, 2020, p. 1–211. [Online] Cited 2024-10-29. Available: https://open.clemson.edu/all_theses/3452/
  16. RUAN, X., LI, B., CHEN, Q., et al. Fundamental considerations of three-level DC-DC converters: Topologies, analyses, and control. IEEE Transactions on Circuits and Systems I: Regular Papers, 2008, vol. 55, no. 11, p. 3733–3743. DOI: 10.1109/TCSI.2008.927218
  17. TREMBLAY, O., DESSAINT, L. A. Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal, 2009, vol. 3, no. 2, p. 289–298. DOI: 10.3390/wevj3020289
  18. ŞEN, M., OZCAN, M., EKER, Y. R. Fuzzy logic-based energy management system for regenerative braking of electric vehicles with hybrid energy storage system. Applied Sciences, 2024, vol. 14, no. 7, p. 1–25. DOI: 10.3390/app14073077
  19. BIEDKA, H., RAFAL, K. Control algorithms of hybrid energy storage system based on fuzzy logic. In 2021 Progress in Applied Electrical Engineering (PAEE 2021). Koscielisko (Poland), 2021, p. 1–5. DOI: 10.1109/PAEE53366.2021.9497387
  20. RAHMAN, A. U., ZEHRA, S. S., AHMAD, I., et al. Fuzzy super twisting sliding mode-based energy management and control of hybrid energy storage system in electric vehicle considering fuel economy. Journal of Energy Storage, 2021, vol. 37, p. 1–14. DOI: 10.1016/j.est.2021.102468
  21. VU, H.-G. DC-link voltage control of voltage source converter by using PI controller with anti-windup (in Vietnamese). Journal of Science and Technology University Da Nang, 2018, vol. 11, no. 1, p. 18–21.
  22. VU, H. G., YAHOUI, H., CHOROT, T., et al. Control active and reactive power of Voltage Source Inverter (VSI). In 2nd International Symposium on Environment Friendly Energies and Applications (EFEA). Newcastle upon Tyne (UK), 2012, p. 308 to 311. DOI: 10.1109/EFEA.2012.6294057

Keywords: Converters, energy storage systems, power distribution networks, supercapacitors

H. Du, F. Wan, V. Mordachev, E.Sinkevich, X. Chen, B. Ravelo [references] [full-text] [DOI: 10.13164/re.2024.0669] [Download Citations]
EMI Characterization from GaN Power Amplifier Nonlinearity Test for 16-QAM 5G Communication

Today, the anywhere, anyhow and anytime application scenarios of 5G system force designer to challenge on electromagnetic interference (EMI) requirements. Despite the technological progress, relevant test techniques are necessary to minimize the future communication system EMI risk. In this paper, the EMI characterization from nonlinearity (NLT) of 5G system Gallium Nitride (GaN) power amplifier (PA) is studied. Firstly, the PA NLT is evaluated by 1-dB/3-dB/6-dB compression point and 3rd-order intermodulation distortion (IMD3). Then, a measurement platform is built based on vector signal generator and EMI receiver including digital modulation system. According to the adjacent channel leakage ratio (ACLR), error vector magnitude (EVM) and signal-to-noise ratio (SNR), the EMI characteristics of 3.5-GHz carrier signals modulated by 16-Quadrature Amplitude Modulation (16-QAM) distorted by the GaN PA NLT are discussed. Due to the GaN PA 3rd order intermodulation (IM3) product, the SNR degrades from 34.8 dB to 14.6 dB when the input signal power increases from -10 dBm to 6 dBm. The EMI effect is confirmed by significant signal distortion observed with 16-QAM constellation diagram. Research work is currently ongoing for extending the EMI test technique for 6G communication system.

  1. SAAD, W., BENNIS, M., CHEN, M. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 2020, vol. 34, no. 3, p. 134–142. DOI: 10.1109/MNET.001.1900287
  2. WIKLUNDH, K., STENUMGAARD, P. EMC challenges with 6G. In 2022 International Symposium on Electromagnetic Compatibility -EMC Europe. Gothenburg (Sweden), 2022, p. 19–24. DOI: 10.1109/EMCEurope51680.2022.9901285
  3. WIKLUNDH, K., STENUMGAARD, P. EMC challenges for the era of massive Internet of Things. IEEE Electromagnetic Compatibility Magazine, 2019, vol. 8, no. 2, p. 65–74. DOI: 10.1109/MEMC.2019.8753447
  4. YAKLAF, S. K. A., TARMISSI, K. S., SHASHOA, N. A. A. 6G mobile communications systems: Requirements, specifications, challenges, applications, and technologies. In IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MISTA. Tripoli (Libya), 2021, p. 679–683. DOI: 10.1109/MISTA52233.2021.9464470
  5. MORDACHEV, V., SINKEVICH, E., YATSKEVICH, Y., et al. Simulation of nonlinear interference in aircraft systems operating in complex electromagnetic environment created by land-based and air-based wireless systems. In 2017 International Symposium on Electromagnetic Compatibility - EMC EUROPE. Angers (France), 2017, p. 1–6. DOI: 10.1109/EMCEurope.2017.8094819
  6. SINKEVICH, E. V. Discrete nonlinear simulation of radio receivers for electromagnetic compatibility analysis and design: Estimation of the signal-to-interference ratio. In 2007 7th International Symposium on Electromagnetic Compatibility and Electromagnetic Ecology. St. Petersburg (Russia), 2007, p. 166–169. DOI: 10.1109/EMCECO.2007.4371677
  7. ABUELMA'ATTI, M. T. Analysis of the effect of radio frequency interference on the DC performance of bipolar operational amplifiers. IEEE Transactions on Electromagnetic Compatibility, 2003, vol. 45, no. 2, p. 453–458. DOI: 10.1109/TEMC.2003.811312
  8. VAN DER HORST, M. J., LINNENBANK, A. C., VAN STAVEREN, A. Amplitude-modulation detection in single-stage negative-feedback amplifiers due to interfering out-of-band signals. IEEE Transactions on Electromagnetic Compatibility, 2005, vol. 47, no. 1, p. 34–44. DOI: 10.1109/TEMC.2004.842116
  9. FIORI, F. A new nonlinear model of EMI-induced distortion phenomena in feedback CMOS operational amplifiers. IEEE Transactions on Electromagnetic Compatibility, 2002, vol. 44, no. 4, p. 495–502. DOI: 10.1109/TEMC.2002.804766
  10. YEH, M.-L., LIOU, W.-R., HSIEH, H.-P., et al. An electromagnetic interference (EMI) reduced high-efficiency switching power amplifier. IEEE Transactions on Power Electronics, 2010, vol. 25, no. 3, p. 710–718. DOI: 10.1109/TPEL.2009.2035622
  11. BAYRAM, Y., VOLAKIS, J. L., MYOUNG, S. K., et al. High power EMI on RF amplifier and digital modulation schemes. IEEE Transactions on Electromagnetic Compatibility, 2008, vol. 50, no. 4, p. 849–860. DOI: 10.1109/TEMC.2008.2004600
  12. PARESCHI, F., ROVATTI, R., SETTI, G. EMI reduction via spread spectrum in DC/DC converters: State of the art, optimization, and tradeoffs. IEEE Access, 2015, vol. 3, p. 2857–2874. DOI: 10.1109/ACCESS.2015.2512383
  13. AUER, M., KARACA, T. Spread spectrum techniques for class-D audio amplifiers to reduce EMI. Elektrotechnik und Informationstechnik, 2016, vol. 133, p. 43–47. DOI: 10.1007/s00502-015-0384-4
  14. JIN, C., TAN, M. T., SEE, K. Y. Filterless class-D amplifier with pseudorandomized carrier frequency modulation for EMI reduction. IEEE Transactions on Electromagnetic Compatibility, 2013, vol. 55, no. 1, p. 74–80. DOI: 10.1109/TEMC.2012.2212907
  15. MRAD, R., PILLONNET, G., MOREL, F., et al. Predicting the impact of magnetic components used for EMI suppression on the base-band of a power amplifier. IEEE Transactions on Power Electronics, 2015, vol. 30, no. 8, p. 4199–4208. DOI: 10.1109/TPEL.2014.2351421
  16. FILION, B., NGUYEN, A. T., RUSCH, L. A., et al. Postcompensation of nonlinear distortions of 64-QAM signals in a semiconductor-based wavelength converter. Journal of Lightwave Technology, 2016, vol. 34, no. 9, p. 2127–2138. DOI: 10.1109/JLT.2016.2523680
  17. VANCAILLIE, L., KILCHYTSKA, V., ALVARADO, J., et al. Characterization and design methodology for low-distortion MOSFET-C analog structures in multithreshold deep submicrometer SOI CMOS technologies. IEEE Transactions on Electron Devices, 2006, vol. 53, no. 2, p. 263–269. DOI:
  18. 10.1109/TED.2005.861725
  19. PALUMBO, G., PENNISI, S., High-frequency harmonic distortion in feedback amplifiers: Analysis and applications. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 2003, vol. 50, no. 3, p. 328–340. DOI: 10.1109/TCSI.2003.808835
  20. MAFFEZZONI, P. Efficient multiparameter sensitivity computation of amplifier harmonic distortion. IEEE Transactions on Circuits and Systems II: Express Briefs, 2007, vol. 54, no. 3, p. 257–261. DOI: 10.1109/TCSII.2006.888725
  21. MIAO, Y., ZHANG, Y. Distortion modeling of feedback two-stage amplifier compensated with Miller capacitor and nulling resistor. Transactions on Circuits and Systems I: Regular Papers, 2012, vol. 59, no. 1, p. 93–105. DOI: 10.1109/TCSI.2011.2161393
  22. SHI, G. Symbolic distortion analysis of multistage amplifiers. IEEE Transactions on Circuits and Systems I: Regular Papers, 2019, vol. 66, no. 1, p. 369–382. DOI: 10.1109/TCSI.2018.2859242
  23. BAXEVANAKIS, D., SOTIRIADIS, P. P. A general time-domain method for harmonic distortion estimation in CMOS circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, vol. 40, no. 1, p. 157–170. DOI: 10.1109/TCAD.2020.2988414
  24. CELIK, A., ZHANG, Z., SOTIRIADIS, P. P. A state-space approach to intermodulation distortion estimation in fully balanced bandpass Gm-C filters with weak nonlinearities. IEEE Transactions on Circuits and Systems I: Regular Papers, 2007, vol. 54, no. 4, p. 829–844. DOI: 10.1109/TCSI.2006.887630
  25. PINI, G., MANSTRETTA, D., CASTELLO, R. Analysis and design of a 20-MHz bandwidth, 50.5-dBm OOB-IIP3, and 5.4-mW TIA for SAW-less receivers. IEEE Journal of Solid-State Circuits, 2018, vol. 53, no. 5, p. 1468–1480. DOI:10.1109/JSSC.2018.2791489
  26. XIAO, Z., JING, X., A novel characteristic parameter approach for analysis and design of linear components in nonlinear systems. IEEE Transactions on Signal Processing, 2016, vol. 64, no. 10, p. 2528–2540. DOI: 10.1109/TSP.2016.2526967
  27. FIORI, F. EMI-induced distortion of baseband signals in current feedback instrumentation amplifiers. IEEE Transactions on Electromagnetic Compatibility, 2018, vol. 60, no. 3, p. 605–612. DOI: 10.1109/TEMC.2017.2736537
  28. FIORI, F. L., CROVETTI, P. S. Prediction of high-power EMI effects in CMOS operational amplifiers. IEEE Transactions on Electromagnetic Compatibility, 2006, vol. 48, no. 1, p. 153–160. DOI: 10.1109/TEMC.2006.870690
  29. GOLUBOVICH, D., CHERMOSHENTSEV, S. Virtual testing of the emission of electromagnetic interference from electronic means according to the electromagnetic compatibility requirements. In 2019 International Conference on Electrotechnical Complexes and Systems (ICOECS). Ufa (Russia), 2019, p. 1–4. DOI: 10.1109/ICOECS46375.2019.8950005
  30. PARVIS, M., PERRONE, G., VALLAN, A. A precompliance EMC test-set based on a sampling oscilloscope. IEEE Transactions on Instrumentation and Measurement, 2003, vol. 54, no. 4, p. 1220 to 1223. DOI: 10.1109/TIM.2003.816839
  31. MORDACHEV, V., SINKEVICH, E. Experimental analysis of radio receiver susceptibility to out-of-band interference by means of double-frequency test system. In 10th International Symposium on Electromagnetic Compatibility. York (UK), 2011, p. 405–411.
  32. SINKEVICH, E., MORDACHEV, V. Investigation of the transmitter susceptibility to reverse intermodulation by the use of double-frequency diagrams. In 2015 IEEE International Symposium on Electromagnetic Compatibility (EMC). Dresden (Germany), 2015, p. 1159–1164. DOI: 10.1109/ISEMC.2015.7256333
  33. 3GPP TS 38.104 V18.3.0. 3rd generation partnership project; technical specification group radio access network, NR, base station (BS) radio transmission and reception (Release 18). 3rd Generation Partnership Project (3GPP). Sep. 2023.
  34. PROAKIS, J. G. Digital Communications. 4th ed. New York (USA): McGraw-Hill, 2001. ISBN: 978-0071181839
  35. JOVANOVIC, B., MILENKOVIC, S. Transmitter IQ imbalance mitigation and PA linearization in software defined radios. Radioengineering, 2022, vol. 31, no. 1, p. 144–154. DOI: 10.13164/re.2022.0144
  36. CHEN, S., HONG, X., KHALAF, E., et al. Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier. Digital Signal Processing, 2015, vol. 40, p. 238–249. DOI: 10.1016/j.dsp.2015.02.006
  37. DUBOIS, T., LAURIN, J.-J., RAOULT, J., et al. Effect of low and high power continuous wave electromagnetic interference on a microwave oscillator system: From VCO to PLL to QPSK receiver. IEEE Transactions on Electromagnetic Compatibility, 2014, vol. 56, no. 2, p. 286–293. DOI: 10.1109/TEMC.2013.2280670
  38. MAJIDI, M., MOHAMMADI, A., ABDIPOUR, A. Analysis of the power amplifier nonlinearity on the power allocation in cognitive radio networks. IEEE Transactions on Communications, 2014, vol. 62, no. 2, p. 467–477. DOI:10.1109/TCOMM.2014.011114.130094
  39. VAN DE BEEK, S., LEFERINK, F. Robustness of a TETRA base station receiver against intentional EMI. IEEE Transactions on Electromagnetic Compatibility, 2015, vol. 57, no. 3, p. 461–469. DOI: 10.1109/TEMC.2015.2406732
  40. PARK, J. J., MOON, J. H., JANG, H. H., et al. Performance analysis of power amplifier nonlinearity on multi-tone SWIPT. IEEE Wireless Communications Letters, 2021, vol. 10, no. 4, p. 765–769. DOI: 10.1109/LWC.2020.3042852
  41. RAVELO, B., WAN, F., NEBHEN, J., et al. Resonance effect reduction with bandpass negative group delay fully passive function. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, vol. 68, no. 7, p. 2364–2368. DOI: 10.1109/TCSII.2021.3059813
  42. RAVELO, B., LALLECHÈRE, S., RAHAJANDRAIBE, W., et al. Electromagnetic cavity resonance equalization with bandpass negative group delay. IEEE Transactions on Electromagnetic Compatibility, 2021, vol. 63, no. 4, p. 1248-1257. DOI: 10.1109/TEMC.2021.3051100
  43. DU, H. GaN Power Amplifier Nonlinear Test Results. [Online] Accessed 2024-09-18. Available at: https://dx.doi.org/10.21227/shge-2674

Keywords: EMI impact, nonlinear characterization, 5G communication, 16-QAM, signal distortion, signal-to-noise ratio (SNR)

J. Wei, L. Yu, Y. Wei, R. Xu [references] [full-text] [DOI: 10.13164/re.2024.0681] [Download Citations]
A Reinforcement Learning-based Intelligent Learning Method for Anti-active Jamming in Frequency Agility Radar

Active jamming's flexibility and variability pose significant challenges for frequency-agility radar (FAR) detection, as it can continuously intercept and retransmit radar signals to suppress or deceive the radar. To tackle this, we propose an intelligent learning method for FAR based on reinforcement learning (RL), integrating signal processing with compressed sensing (CS). We introduce an inter-pulse carrier-frequency hopping combined with intra-pulse sub-frequency coding (IPCFH-IPSFC) signal model to address time-domain discontinuities caused by active jamming, enabling effective mutual masking of pulses through agile waveform parameters. We develop jamming signal models and design four jamming strategies based on two common types of active jamming, providing essential data for the FAR intelligent learning method. To enhance FAR’s adaptive anti-jamming and target detection performance, we propose an RL-based intelligent learning model. This model includes five submodules: signal processing, anti-jamming evaluation, target detection, optimization constraint design, and optimization algorithm design. We apply a proximal policy optimization combined with a generative pre-trained transformer (PPO-GPT) to solve this model, allowing FAR to adaptively learn jamming strategies and optimize IPCFH-IPSFC waveform parameters for effective anti-jamming. Simulation results confirm that our method achieves robust performance and rapid convergence, finding optimal anti-jamming strategies in just 215 training iterations. The FAR effectively counteracts jamming while accurately estimating target range and velocity.

  1. LI, K., JIU, B., PU, W., et al. Neural fictitious self-play for radar antijamming dynamic game with imperfect information. IEEE Transactions on Aerospace and Electronic Systems, 2022, vol. 58, no. 6, p. 5533–5547. DOI: 10.1109/TAES.2022.3175186
  2. LI, K., JIU, B., LIU, H., et al. Game theoretic strategies design for monostatic radar and jammer based on mutual information. IEEE Access,
  3. 2019, vol. 7, p. 72257–72266. DOI: 10.1109/ACCESS.2019.2920398
  4. ZHANG, Y., WEI, Y., YU, L. Interrupted sampling repeater jamming recognition and suppression based on phase-coded signal processing. Signal Processing, 2022, vol. 198, p. 1–7. DOI: 10.1016/j.sigpro.2022.108596
  5. ZHU, Y., ZHANG, Z., LI, B., et al. Analysis of characteristics and suppression methods for self-defense smart noise jamming. Electronics, 2023, vol. 12, no. 15, p. 1–14. DOI: 10.3390/electronics12153270
  6. XIE, Q., LIU, C., MO, Z., et al. A novel pulse-agile waveform design based on random FM waveforms for range sidelobe suppression and range ambiguity mitigation. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–12. DOI: 10.1109/TGRS.2023.3326840
  7. XI, R., MA, D., LIU, X., et al. Intra-pulse frequency coding design for a high-resolution radar against smart noise jamming. Remote Sensing, 2022, vol. 14, no. 20, p. 1–20. DOI: 10.3390/rs14205149
  8. DE MARTINO, A. Introduction to Modern EW Systems. Beijing (China): Electronic Industry Press, 2014.
  9. LIU, S., CAO, Y., YEO, T. S., et al. Range sidelobe suppression for randomized stepped-frequency chirp radar. IEEE Transactions on Aerospace and Electronic Systems, 2021, vol. 57, no. 6, p. 3874–3885. DOI: 10.1109/TAES.2021.3082670
  10. QUAN, Y., WU, Y., LI, Y., et al. Range-Doppler reconstruction for frequency agile and PRF-jittering radar. IET Radar Sonar and Navigation, 2018, vol. 12, no. 3, p. 348–352. DOI: 10.1049/ietrsn.2017.0421
  11. ZHOU, R., XIA, G., YUE, Z., et al. Coherent signal processing method for frequency-agile radar. In 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). Qingdao (China), 2016, p. 431–434. DOI: 10.1109/ICEMI.2015.7494227
  12. KIRK, B., NARAYANAN, R., GALLAGHER, K., et al. Avoidance of time-varying radio frequency interference with software-defined cognitive radar. IEEE Transactions on Aerospace and Electronic Systems, 2019, vol. 55, no. 3, p. 1090–1107. DOI: 10.1109/TAES.2018.2886614
  13. WEI, S., ZHANG, L., LIU, H. Joint frequency and PRF agility waveform optimization for high-resolution ISAR imaging. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–23. DOI: 10.1109/TGRS.2021.3051038
  14. LV, M., CHEN, W., MA, J., et al. Joint random stepped frequency ISAR imaging and autofocusing based on 2D alternating direction method of multipliers. Signal Processing, 2022, vol. 201, p. 1–11. DOI: 10.1016/j.sigpro.2022.108684
  15. WANG, X., WANG, S., LIANG, X., et al., Deep reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 2024, vol. 35, no. 4, p. 5064–5078. DOI: 10.1109/TNNLS.2022.3207346
  16. SHAO, Y., LIEW, S., WANG, T. AlphaSeq: Sequence discovery with deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2020, vol. 31, no. 9, p. 3319 to 3333. DOI: 10.1109/TNNLS.2019.2942951
  17. WEI, J., WEI, Y., YU, L., et al. Radar anti-jamming decision making method based on DDPG-MADDPG algorithm. Remote Sensing, 2023, vol. 15, no. 16, p. 1–25. DOI: 10.3390/rs15164046
  18. AILIYA, WEI, Y., YE, Y. Reinforcement learning-based joint adaptive frequency hopping and pulse-width allocation for radar anti-jamming. In IEEE Radar Conference (RadarConf20). Florence (Italy), 2020, p. 1–6. DOI: 10.1109/RadarConf2043947.2020.9266402
  19. LI, K., JIU, B., LIU, H., et al. Reinforcement learning based antijamming frequency hopping strategies design for cognitive radar. In IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). Qingdao (China), 2018, vol. 14, no. 16, p. 1–5. DOI: 10.1109/ICSPCC.2018.8567751
  20. LI, K., JIU, B., LIU, H., et al. Deep Q-network based anti-jamming strategy design for frequency agile radar. In 2019 International Radar Conference (RADAR). Toulon (France), 2019, p. 1–5. DOI: 10.1109/RADAR41533.2019.171227
  21. AK, S., BRUGGENWIRTH, S. Avoiding jammers: A reinforcement learning approach. In 2020 IEEE International Radar Conference (RADAR). Florence (Italy), 2020, p. 321–326. DOI: 10.1109/RADAR42522.2020.9114797
  22. GENG, J., JIU, B., LI, K., et al. Radar and jammer intelligent game under jamming power dynamic allocation. Remote Sensing, 2023, vol. 15, no. 3, p. 1–26. DOI: 10.3390/rs15030581
  23. LI, K., JIU, B., WANG, P., et al. Radar active antagonism through deep reinforcement learning: A way to address the challenge of mainlobe jamming. Signal Processing, 2021, vol. 186, p. 1–15. DOI: 10.1016/j.sigpro.2021.108130
  24. LI, K., JIU, B., LIU, H., et al. Robust antijamming strategy design for frequency-agile radar against main lobe jamming. Remote Sensing, 2021, vol. 13, no. 15, p. 1–24. DOI: 10.3390/rs13153043
  25. GENG, J., JIU, B., LI, K., et al. Reinforcement learning based radar anti-jamming strategy design against a non-stationary jammer. In 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). Xi'an (China), 2022, p. 1–5. DOI: 10.1109/ICSPCC55723.2022.9984459
  26. ZHOU, K., LI, D., HE, F., et al. A sparse imaging method for frequency agile SAR. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, p.
  27. 1–16. DOI: 10.1109/TGRS.2022.3151079
  28. LIU, H., ZHOU, S., SU, H., et al. Detection performance of spatial-frequency diversity MIMO radar. IEEE Transactions on Aerospace and Electronic Systems, 2014, vol. 50, no. 4, p. 3137 to 3155. DOI: 10.1109/TAES.2013.120040
  29. WEI, S., ZHANG, L., MA, H., et al. Sparse frequency waveform optimization for high-resolution ISAR imaging. IEEE Transactions on Geoscience and Remote Sensing, 2020, vol. 58, no. 1, p. 546–566. DOI: 10.1109/TGRS.2019.2937965
  30. QUAN, Y., LI, Y., WU, Y., et al. Moving target detection for frequency agility radar by sparse reconstruction. Review of Scientific Instruments, 2016, vol. 87, no. 9, p. 1–8. DOI: 10.1063/1.4962700
  31. ZHOU, K., LI, D., SU, Y., et al. Joint design of transmit waveform and mismatch filter in the presence of interrupted sampling repeater jamming. IEEE Signal Processing Letters, 2020, vol. 27, p. 1610–1614. DOI: 10.1109/LSP.2020.3021667
  32. LIU, Z., ZHANG, Q., LI, K. A smart noise jamming suppression method based on atomic dictionary parameter optimization decomposition. Remote Sensing, 2022, vol. 14, no. 8, p. 1–14. DOI: 10.3390/rs14081921
  33. LI, R., WANG, X., LI, G., et al. TEFISTA-Net: A learnable method for high-resolution range profile reconstruction with low frequency ultra-wideband radar. Signal Processing, 2024, vol. 214, p. 1–14. DOI: 10.1016/j.sigpro.2023.109257
  34. POURNAGHSHBAND, R., MODARRES-HASHEMI, M. A novel block compressive sensing algorithm for SAR image formation. Signal Processing, 2023, vol. 210, p. 1–14. DOI: 10.1016/j.sigpro.2023.109053
  35. HUANG, W. A genetic algorithm optimized under sampling method for seismic sparse acquisition and reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–10. DOI: 10.1109/TGRS.2023.3252277
  36. MIGUEZ, J., MARIÑO, I. P., VAZQUEZ, M. A. Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models. Signal Processing, 2018, vol. 142, p. 281–291. DOI: 10.1016/j.sigpro.2017.07.030
  37. ZHENG, J., KURT, M. N., WANG, X. Stochastic integrated actor critic for deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2024, vol. 35, no. 5, p. 6654–6666. DOI: 10.1109/TNNLS.2022.3212273
  38. ZHANG, W., ZHAO, T., ZHAO, Z., et al. Performance analysis of deep reinforcement learning-based intelligent cooperative jamming method confronting multi-functional networked radar. Signal Processing, 2023, vol. 207, p. 1–13. DOI: 10.1016/j.sigpro.2023.108965108965
  39. AKIN, E. Deep reinforcement learning-based multi restricted dynamic-request transportation framework. IEEE Transactions on Neural Networks and Learning Systems, 2023, p. 1–11. DOI: 10.1109/TNNLS.2023.3341471
  40. XIONG, K., ZHANG, T., CUI, G., et al. Coalition game of radar network for multitarget tracking via model-based multiagent reinforcement learning. IEEE Transactions on Aerospace and Electronic Systems, 2023, vol. 59, no. 3, p. 2123–2140. DOI: 10.1109/TAES.2022.3208865

Keywords: Intelligent learning, anti-active jamming, frequency agility radar, reinforcement learning, compressed sensing

Z. Y. Jiang, J. W. Zhang, H. J. Yang, P. Geng [references] [full-text] [DOI: 10.13164/re.2024.0704] [Download Citations]
Secure Power Data Sharing with Fine-grained Control: A Multi-strategy Access Tree Approach

The current data sharing schemes mainly employ Attribute-Based Encryption (ABE) technique to achieve one-to-many access control for power data. However, these schemes suffer from issues such as low encryption efficiency and vulnerability to user attribute tampering. To address these problems, a power data sharing scheme based on multi-strategy access trees is proposed. By combining ABE with symmetric encryption algorithms, specifically employing the Advanced Encryption Standard (AES) in conjunction with Ciphertext-Policy ABE (CP-ABE), a hybrid encryption mechanism is adopted. Building upon an encryption algorithm rooted in multi-strategy access trees, data visitors are categorized into security levels according to roles and regions. Then, a time-constrained attribute encryption scheme is proposed for designated personnel, thereby achieving confidentiality and fine-grained access control for power data. Analysis results indicate that the proposed scheme enables secure sharing of power data and is highly suitable for resource-limited power terminal devices.

  1. SAIDI, A., NOUALI, O., AMIRA, A. SHARE-ABE: An efficient and secure data sharing framework based on ciphertext-policy attribute-based encryption and Fog computing. Cluster Computing, 2022, vol. 25, p. 167–185. DOI: 10.1007/s10586-021-03382-5
  2. ANCY, P. R., KRISHNA, A. V. N., BALACHANDRAN, K. B. M. An efficient access policy with multi-linear secret-sharing scheme in ciphertext-policy attribute-based encryption. Journal of Theoretical and Applied Information Technology, 2022, vol. 100, no. 5, p. 1404–1411. ISSN: 1992-8645
  3. XIE, S., ZHANG, L., WU, Q., et al. Flexibly expressive and revocable multi-authority KP-ABE scheme from RLWE for Internet of Medical Things. Journal of Systems Architecture, 2024, vol. 152, p. 1–12. DOI:10.1016/j.sysarc.2024.103179
  4. MISHRA, B., JENA, D., PATNAIK, S. Fine-grained access control of files stored in cloud storage with traceable and revocable multiauthority CP-ABE scheme. International Journal of Grid and Utility Computing, 2023, vol. 14, no. 4, p. 320–338. DOI: 10.1504/IJGUC.2023.10057953
  5. FADLULLAH, Z. M., KATO, N., LU, R., et al. Toward secure targeted broadcast in smart grid. IEEE Communications Magazine, 2012, vol. 50, no. 5, p.
  6. 150–156. DOI: 10.1109/MCOM.2012.6194396
  7. HUANG, Q., YAN, G., WEI, Q. Attribute-based expressive and ranked keyword search over encrypted documents in cloud computing. IEEE Transactions on Services Computing, 2023, vol. 16, no. 2, p. 957–968. DOI: 10.1109/TSC.2022.3149761
  8. ZHANG, L. Y., YANG, G., SONG, C., et al. Accountable multiauthority attribute-based data access control in smart grids. Journal of King Saud University - Computer and Information Sciences, 2023, vol. 35, no. 7, p. 1–12. DOI: 10.1016/j.jksuci.2023.101597
  9. SITHARTHAN, R., VIMAL, S., VERMA, A., et al. Smart microgrid with the Internet of Things for adequate energy management and analysis. Computers & Electrical Engineering, 2023, vol. 106, p. 1–11. DOI: 10.1016/j.compeleceng.2022.108556
  10. BEIMEL, A., FARRAS, O., MINTZ, Y., et al. Linear secret-sharing schemes for forbidden graph access structures. IEEE Transactions on Information Theory, 2022, vol. 68, no. 3, p. 2083–2100. DOI: 10.1109/TIT.2021.3132917
  11. BHAVANI, N. G., KUMAR, R., BHAWANI, S. P., et al. Design and implementation of IoT integrated monitoring and control system of renewable energy in smart grid for sustainable computing network. Sustainable Computing: Informatics and Systems, 2022, vol. 35, p. 1–10. DOI: 10.1016/j.suscom.2022.100769
  12. LIU, K. P., WANG, C. F., ZHOU, X. T. Decentralizing access control system for data sharing in smart grid. High-Confidence Computing, 2023, vol. 3,
  13. no. 2, p. 1–8. DOI: 10.1016/j.hcc.2023.100113
  14. ZUO, Y. T., KANG, Z. Z., XU, J., et al. BCAS: A blockchain-based ciphertext-policy attribute-based encryption scheme for cloud data security sharing. International Journal of Distributed Sensor Networks, 2021, vol. 17, no. 3, p. 1–16. DOI: 10.1177/1550147721999616
  15. XIONG, Y., LUO, M. X. Searchable encryption scheme for large data sets in cloud storage environment. Radioengineering, 2024, vol. 33, no. 2, p. 223–235. DOI: 10.13164/re.2024.0223
  16. JIANG, X., SUN, A., SUN, Y., et al. A trust-based hierarchical consensus mechanism for consortium blockchain in smart grid. Tsinghua Science and Technology, 2023, vol. 28, no. 1, p. 69–81. DOI: 10.26599/TST.2021.9010074
  17. ZHAO, M., DING, Y., TANG, S., et al. A blockchain-based framework for privacy-preserving and verifiable billing in smart grid. Peer-to-Peer Networking and Applications, 2023, vol. 16, p. 142–155. DOI: 10.1007/s12083-022-01379-4
  18. KAMAL, N. F., AL-ALI, A. K., AL-ALI, A., et al. LPPDA: A lightweight privacy-preserving data aggregation protocol for smart grids. IEEE Access, 2023, vol. 11, p. 95358–95367. DOI: 10.1109/ACCESS.2023.3311140
  19. MARTINASEK, Z., ZEMAN, V., MALINA, L., et al. k-nearest neighbors algorithm in profiling power analysis attacks. Radioengineering, 2016, vol. 25, no. 2, p. 365–382. DOI: 10.13164/RE.2016.0365
  20. PREMKAMAL, P. K., PASUPULETI, S. K., ALPHONSE, P. J. A. Efficient escrow-free CP-ABE with constant size ciphertext and secret key for big data storage in cloud. International Journal of Cloud Applications and Computing (IJCAC), 2020, vol. 10, no. 1, p. 28–45. DOI: 10.4018/IJCAC.2020010103
  21. NANJO, Y., SHIRASE, M., KODERA, Y., et al. Efficient final exponentiation for cyclotomic families of pairing-friendly elliptic curves with any prime embedding degrees. International Journal of Networking and Computing, 2022, vol. 12, no. 2, p. 317–338. DOI: 10.15803/ijnc.12.2_317
  22. WANG, B. W., LI, W., XIONG, N. N. Time-based access control for multi-attribute data in Internet of Things. Mobile Networks and Applications, 2021, vol. 26, p. 797–807. DOI: 10.1007/s11036-01901327-2

Keywords: Attribute-based encryption, access control, fine granularity; threshold trees, smart grid

X. M. Liu, Y. L. Song, J. W. Zhu, F. Shu, Y. W Qian [references] [full-text] [DOI: 10.13164/re.2024.0713] [Download Citations]
An Efficient Deep Learning Model for Automatic Modulation Recognition

Automatic Modulation Classification (AMC) has emerged as a critical research domain with wide-ranging applications in both civilian and military contexts. With the advent of artificial intelligence, deep learning techniques have gained prominence in AMC due to their unparalleled ability to automatically extract relevant features. However, most contemporary AMC models rely heavily on downsampling strategies to increase the receptive field while reducing computational complexity. Empirical evidence indicates that progressive downsampling substantially reduces the spatial resolution of feature maps, leading to poor generalization, particularly for closely related modulation schemes. To address these challenges, this paper proposes a novel Multiscale Dilated Pyramid Module (MDPM). In contrast to traditional downsampling techniques, MDPM mitigates resolution loss and retains a broader range of features, facilitating more comprehensive recognition. Furthermore, the multiscale features captured by MDPM enhance the robustness of the model to noise, thereby improving classification performance in noisy environments. The model's efficiency is further optimized through the integration of group convolutions and channel shuffle techniques. Extensive experimental results and evaluations confirm that the MDPM-based approach surpasses state-of-the-art methods, underscoring its significant potential for practical deployment. The signal data¬base and model can be freely accessed at https://pan.baidu.com/s/1g_HQXcRXshrT8nwKUNDYrQ?pwd=9ug6.

  1. WANG, Q., DU, P., YANG, J., et al. Transferred deep learning based waveform recognition for cognitive passive radar. Signal Processing, 2019, vol. 155, p. 259–267. DOI: 10.1016/j.sigpro.2018.09.038
  2. LUNDEN, J., KOIVUNEN, V. Automatic radar waveform recognition. IEEE Journal of Selected Topics in Signal Processing, 2007, vol. 1, no. 1, p. 124–136. DOI: 10.1109/JSTSP.2007.897055
  3. XU, C., ZHANG, J., ZHOU, Q., et al. Recognition of radar signals based on AF grids and geometric shape constraint. Signal Processing, 2019, vol. 157, p. 30–44. DOI: 10.1016/j.sigpro.2018.11.004
  4. CHEN, K., CHEN, S., ZHANG, S., et al. Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis. Signal, Image and Video Processing, 2023, vol. 17, no. 6, p. 3053–3061. DOI: 10.1007/s11760-023-02526-x
  5. LEE, Y. S., BANG, C. C. Framework for the classification of imbalanced structured data using under-sampling and convolutional neural network. Information Systems Frontiers, 2022, vol. 24, p. 1795–1809. DOI: 10.1007/s10796-021-10195-9
  6. O’SHEA, T. J., ROY, T., CLANCY, T. C. Over-the-air deep learning based radio signal classification. IEEE Journal of Selected Topics in Signal Processing, 2018, vol. 12, no. 1, p. 168–179. DOI: 10.1109/JSTSP.2018.2797022
  7. WANG, Y., LIU, M., YANG, J., et al. Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Transactions on Vehicular Technology, 2019, vol. 68, no. 4, p. 4074–4077. DOI: 10.1109/TVT.2019.2900460
  8. ZHANG, F., LUO, C., XU, J., et al. An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation. IEEE Communications Letters, 2021, vol. 25, no. 10, p. 3287–3290. DOI: 10.1109/LCOMM.2021.3102656
  9. CHEN, K., ZHU, L., CHEN, S., et al. Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution. Measurement, 2021, vol. 185, p. 1–8. DOI: 10.1016/j.measurement.2021.109945
  10. HUYNH-THE, T., DOAN, V. S., HUA, C. H., et al. Accurate LPI radar waveform recognition with CWD-TFA for deep convolutional network. IEEE Wireless Communications Letters, 2021, vol. 10, no. 8, p. 1638–1642. DOI: 10.1109/LWC.2021.3075880
  11. ZHANG, M., DIAO, M., GUO, L. Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access, 2017, vol. 5, p. 11074–11082. DOI: 10.1109/ACCESS.2017.2716191
  12. CHEN, K., ZHANG, J., CHEN, S., et al. Automatic modulation classification of radar signals utilizing X-net. Digital Signal Processing, 2022, vol. 123, p. 1–10. DOI: 10.1016/j.dsp.2022.103396
  13. HOANG, L. M., KIM, M., KONG, S. H. Automatic recognition of general LPI radar waveform using SSD and supplementary classifier. IEEE Transactions on Signal Processing, 2019, vol. 67, no. 13, p. 3516–3530. DOI: 10.1109/TSP.2019.2918983
  14. CHEN, K., WANG, L., ZHANG, J., et al. Semantic learning for analysis of overlapping LPI radar signals. IEEE Transactions on Instrumentation and Measurement, 2023, vol. 72, p. 1–15. DOI: 10.1109/TIM.2023.3242013
  15. ZHANG, Z., WANG, C., GAN, C., et al. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 2019, vol. 5, no. 3, p. 469 to 478. DOI: 10.1109/TSIPN.2019.2900201
  16. YU, F., KOLTUN, V., FUNKHOUSER, T. Dilated residual networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu (HI, USA), 2017, p. 636–644. DOI: 10.1109/CVPR.2017.75
  17. CHEN, L., PAPANDREOU, G., SCHROFF, F., et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint, 2017, p. 1–14. DOI: 10.48550/arXiv.1706.05587
  18. WANG, Q., WU, B., ZHU, P., et al. ECA-Net: efficient channel attention for deep convolutional neural networks. In 2020 IEEE Conference on Computer Vision and Pattern Recognition. Seattle (WA, USA), 2020, p. 11531–11539. DOI: 10.1109/CVPR42600.2020.01155
  19. HU, J., SHEN. L., ALBANIE, S., et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, vol. 42, no. 8, p. 2011–2023. DOI: 10.1109/TPAMI.2019.2913372
  20. ZHANG, X., ZHOU, X., LIN, M., et al. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City (UT, USA), 2018, p. 6848–6856. DOI: 10.1109/CVPR.2018.00716
  21. CHEN, K., ZHANG, J., CHEN, S., et al. Deep metric learning for robust radar signal recognition. Digital Signal Processing, 2023, vol. 137, p. 1–8. DOI: 10.1016/j.dsp.2023.104017
  22. WEI, S., QU, Q., ZENG, X., et al. Self-attention Bi-LSTM networks for radar signal modulation recognition. IEEE Transactions on Microwave Theory and Techniques, 2021, vol. 69, no. 11, p. 5160–5172. DOI: 10.1109/TMTT.2021.3112199
  23. KUBANKOVA, A., KUBANEK, D. Extended method of digital modulation recognition and its testing. Radioengineering, 2011, vol. 20, no. 1, p. 25–30. ISSN: 1210-2512

Keywords: Automatic modulation classification, deep learning, spatial resolution, multi-scale dilated pyramid module, group convolution

C. Ding, S. Chen, H. Liu, Z. Luo, J. Zhang [references] [full-text] [DOI: 10.13164/re.2024.0721] [Download Citations]
Infrared Small Target Detection, High-Precision Localization and Segmentation: Using TDU Kernel

Aiming at the challenge of infrared small target detection with different shape and size under the different scene, a novel algorithm architecture is proposed using the kernel of Target Detection Unit (TDU). The TDU incorporates the fractal geometry design and dual-scale structure, which can execute three main sub-tasks: preliminary target detection, target localization with high precision and target segmentation by pixel-level. First, the principle establishes a dual-scale target detection structure, selects the central point, decomposes the scale information and constructs the Integrated Local Contrast Saliency (ILCS) map, the target preliminary result is obtained by the visual attention mechanism of “top to bottom”. Second, the principle adopts the scale-recursion algorithm by the mechanism of “bottom to up” to locate the target precisely from the preliminary result along with Area Optimal Recommend Mechanism (AORM) strategy. At last, the separated local histogram is used to segment the target by per-pixel with suitable threshold. From the experimental results, conducted across five different types of infrared-scenes including infrared sky scene, infrared maritime scene, backlight illuminance scene, infrared scene with interference and infrared scene with small & dim target, we observe the performance of high accuracy rate and remarkable robustness.

  1. LI, Y., ZHANG, Y. Robust infrared small target detection using local steering kernel reconstruction. Pattern Recognition, 2018, vol. 77, p. 113–125. DOI: 10.1016/j.patcog.2017.12.012
  2. ZHAO, M., LI, W., LI, L., et al. Infrared small UAV target detection via isolation forest. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–16. DOI: 10.1109/TGRS.2023.3321723
  3. CHEN, Y., WANG, H., PANG, Y., et al. An infrared small target detection method based on a weighted human visual comparison mechanism for safety monitoring. Remote Sensing, 2023, vol. 15, no. 11, p. 1–23. DOI: 10.3390/rs15112922
  4. BAI, Y., LI, R., GOU, S., et al. Cross-connected bidirectional pyramid network for infrared small-dim target detection. IEEE Geoscience and Remote Sensing Letters, 2022, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2022.3145577
  5. LI, Z., LIAO, S., WU, M., et al. Small and dim infrared moving target detection based on spatial-temporal saliency. Optik, 2022, vol. 270, p. 1–12. DOI: 10.1016/j.ijleo.2022.170010
  6. LI, Z., LIAO, S., WU, M., et al. SFD-SR: Strengthened feature description and stabilized re-detection-based infrared small-dim target tracking algorithm. Soft Computing, 2024, vol. 28, no. 2, p. 963–979. DOI: 10.1007/s00500-023-09307-1
  7. SUN, J., WANG, J., HAO, Z., et al. AC-LSTM: Anomaly state perception of infrared point targets based on CNN+LSTM. Remote Sensing, 2022, vol. 14, no. 13, p. 1–19. DOI: 10.3390/rs14133221
  8. LI, B., XIAO, C., WANG, L., et al. Dense nested attention network for infrared small target detection. IEEE Transactions on Image Processing, 2022, vol. 32, p. 1745–1758. DOI: 10.1109/TIP.2022.3199107
  9. JIANG, K., WANG, Z., YI, P., et al. Deep distillation recursive network for remote sensing imagery super-resolution. Remote Sensing, 2018, vol. 10, p. 1–23. DOI: 10.3390/rs10111700
  10. LI, S., ZHANG, K., DUAN, P., et al. Hyperspectral anomaly detection with kernel isolation forest. IEEE Transactions on Geoscience and Remote Sensing, 2019, vol. 58, no. 1, p. 319–329. DOI: 10.1109/TGRS.2019.2936308
  11. CUI, H., LI, L., LIU, X., et al. Infrared small target detection based on weighted three-layer window local contrast. IEEE Geoscience and Remote Sensing Letters, 2022, vol. 19, p. 1–5. DOI: 10.1109/LGRS.2021.3133649
  12. QIU, Z., MA, Y., FAN, F., et al. A pixel-level local contrast measure for infrared small target detection. Defence Technology, 2022, vol. 18, no. 9, p. 1589–1601. DOI: 10.1016/j.dt.2021.07.002
  13. LIU, Y., RAO, X., HU, J., et al. A weak target detection algorithm IAR-STFT based on correlated k-distribution sea clutter model. Radioengineering, 2023, vol. 32, no. 1, p. 33–43. DOI: 10.13164/re.2023.0033
  14. XU, Y., WAN, M., ZHANG, X., et al. Infrared small target detection based on local contrast-weighted multidirectional derivative. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–16. DOI: 10.1109/TGRS.2023.3244784
  15. YANG, P., DONG, L., XU, W. Infrared small maritime target detection based on integrated target saliency measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, vol. 14, p. 2369–2386. DOI: 10.1109/JSTARS.2021.3049847
  16. HU, X., WANG, X., YANG, X., et al. An infrared target intrusion detection method based on feature fusion and enhancement. Defence Technology, 2020, vol. 16, p. 737–746. DOI: 10.1016/j.dt.2019.10.005
  17. KONG, X., YANG, C., CAO, S., et al. Infrared small target detection via nonconvex tensor fibered rank approximation. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–21. DOI: 10.1109/TGRS.2021.3068465
  18. CHAN, Y. T. Maritime filtering for images and videos. Signal Processing: Image Communication, 2021, vol. 99, p. 1–13. DOI: 10.1016/j.image.2021.116477
  19. FAN, X., QIN, W., LI, J., et al. Dim and small target detection based on spatio-temporal filtering and high-order energy estimation. IEEE Photonics Journal, 2023, vol. 15, no. 2, p. 1–20. DOI: 10.1109/JPHOT.2023.3242991
  20. CHAN, Y. T. Comprehensive comparative evaluation of background subtraction algorithms in open sea environments. Computer Vision and Image Understanding, 2021, vol. 202, p. 1 to 16. DOI: 10.1016/j.cviu.2020.103101
  21. LUO, Y., LI, X., CHEN, S. Feedback spatial-temporal infrared small target detection based on orthogonal subspace projection. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–19. DOI: 10.1109/TGRS.2024.3368099
  22. CHEN, C., LI, H., WEI, Y., et al. A local contrast method for small infrared target detection. IEEE Transactions on Geoscience and Remote Sensing, 2014, vol. 52, no. 1, p. 574–581. DOI: 10.1109/TGRS.2013.2242477
  23. TANG, Y., XIONG, K., WANG, C. Fast infrared small target detection based on global contrast measure using dilate operation. IEEE Geoscience and Remote Sensing Letters, 2023, vol. 20, p. 1–5. DOI: 10.1109/LGRS.2023.3233958
  24. AGHAZIYARATI, S., MORADI, S., TALEBI, H. Small infrared target detection using absolute average difference weighted by cumulative directional derivatives. Infrared Physics & Technology, 2019, vol. 101, p. 78–87. DOI: 10.1016/j.infrared.2019.06.003
  25. MORADI, S., MOALLEM, P., SABAHI, M. F. Fast and robust small infrared target detection using absolute directional mean difference algorithm. Signal Processing, 2020, vol. 177, p. 1–9. DOI: 10.1016/j.sigpro.2020.107727
  26. ZHANG, H., ZHANG, L., YUAN, D., et al. Infrared small target detection based on local intensity and gradient properties. Infrared Physics & Technology, 2018, vol. 89, p. 88–96. DOI: 10.1016/j.infrared.2017.12.018
  27. XIA, C., LI, X., ZHAO, L., et al. Infrared small target detection based on multiscale local contrast measure using local energy factor. IEEE Geoscience and Remote Sensing Letters, 2020, vol. 17, no. 1, p. 157–161. DOI: 10.1109/LGRS.2019.2914432
  28. ZHANG, Z., DING, C., GAO, Z., et al. ANLPT: Self-adaptive and non-local patch-tensor model for infrared small target detection. Remote Sensing, 2023, vol. 15, no. 4, p. 1–24. DOI: 10.3390/rs15041021
  29. TUNC, S., ILGIN, H. A. Dim target detection in infrared images using saliency algorithms. Radioengineering, 2019, vol. 28, no. 3, p. 635–642. DOI: 10.13164/re.2019.0635
  30. HE, Z., ZHANG, L., GAO, X., et al. Multi-adversarial faster RCNN with paradigm teacher for unrestricted object detection. International Journal of Computer Vision, 2023, vol. 131, no. 3, p. 680–700. DOI: 10.1007/s11263-022-01728-z
  31. WANG, C.-Y., BOCHKOVSKIY, A., LIAO, H.-Y. M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver (BC, Canada), 2023, p. 7464–7475. DOI: 10.1109/CVPR52729.2023.00721
  32. ZHANG, W., LI, Z., LI, G., et al. GACNet: Generate adversarial driven cross-aware network for hyperspectral wheat variety identification. IEEE Transactions on Geoscience and Remote Sensing, 2024, vol. 62, p. 1–14. DOI: 10.1109/TGRS.2023.3347745
  33. ZHANG, W., ZHAO, W. LI, J., et al. CVANet: Cascaded visual attention network for single image super-resolution. Neural Networks, 2024, vol. 170,
  34. p. 622–634. DOI: 10.1016/j.neunet.2023.11.049
  35. YANG, J., YANG, M. Top-down visual saliency via joint CRF and dictionary learning. IEEE Transactions on Pattern Analysis and Machine Learning, 2016, vol. 39, no. 3, p. 576–588. DOI: 10.1109/TPAMI.2016.2547384
  36. SIRIS, A., JIAO, J., TAM, G. K. L., et al. Inferring attention shifts for salient instance ranking. International Journal of Computer Vision, 2024, vol. 132, p. 964–986. DOI: 10.1007/s11263-02301906-7
  37. SUN, X., GUO, L., ZHANG, W., et al. Dataset for small infrared moving target detection under complex background. V3. Science Data Bank, 2024. [Online] Cited 2024-09-20. Available at: https://doi.org/10.11922/sciencedb.j00001.00231. DOI: 10.11922/sciencedb.j00001.00231
  38. HUI, B., SONG, Z., FAN, H., et al. A dataset for infrared image dim-small aircraft target detection and tracking underground / air background V1. Science Data Bank, 2019. [Online] Cited 2024-09-20. Available at: https://doi.org/10.11922/sciencedb.902. DOI: 10.11922/sciencedb.902
  39. SUN, Y., YANG, J., LI, M., et al. Infrared small target detection via spatial–temporal infrared patch-tensor model and weighted Schatten p-norm minimization. Infrared Physics & Technology, 2019, vol. 102, p. 1–10. DOI: 10.1016/j.infrared.2019.103050

Keywords: Infrared target detection, target detection unit, target localization with high-precision, target segmentation by per-pixel, area optimal recommend mechanism

A. Boularas, M. Flissi, K. Rouabah, B. Hammache [references] [full-text] [DOI: 10.13164/re.2024.0733] [Download Citations]
Improved Gain of a Compact Slot Antenna using an FSS Reflector for GNSS L1/E1/B1/G1 bands

In this paper, a compact stepped slot antenna, for Global Navigation Satellite System (GNSS) applications, is proposed. The latter one, which operates in the frequency range from 1.49 GHz to 1.66 GHz, is designed to cover five GNSS bands namely GPS-L1, Galileo-E1, GLONASS-G1, BEIDOU-B1 and EGNOS-L1. The proposed composite design is carried out in three phases. Firstly, a compact slot antenna, having a size of 58.0times52.0times1.6 mm, is designed on FR4 substrate to generate the five GNSS bands. Secondly, a compact Frequency Selective Surface (FSS) reflector, with 18.0times18.0times1.6 mm unit size, is designed to produce a stop band response matching all these bands. Finally, a single layer FSS, consisting of 9times9 units, is combined with the optimized antenna to achieve a high-gain directional antenna structure. The final proposed combination has been investigated using the High Frequency Structure Simulator (HFSS) and validated by Computer Simulation Technology (CST) Microwave Studio. Besides, a prototype antenna is fabricated and validated by measurements. The measurement results, which illustrate a good agreement with those corresponding to the simulations, have shown that the proposed composite antenna exhibits a directional radiation pattern with a high peak gain of 7.3 dBi and a maximum gain improvement of 5.65 dBi at 1.57 GHz. Furthermore, it offers good radiation efficiency in the operating bands, which makes it a good candidate for multi-systems GNSS receivers, especially to reduce the different interferences by enhancing the antenna radiation characteristics.

  1. CATA, M. Exactly positioning determination using global navigation satellite system (GNSS). Analele Ştiinţifice ale Universităţii (Ovidius) Constanţa, Ser.: Matematică, 2003, vol. 11, no. 2, p. 43–50. [Online] Available at: http://eudml.org/doc/125833
  2. CHEN, X., PARINI, C. G., COLLINS, B., et al. Antennas for Global Navigation Satellite Systems. Wiley, 2012. DOI: 10.1002/9781119969518
  3. CHEBIR, S., AIDEL, S., ROUABAH, K., et al. GNSS signals acquisition and tracking in unfavorable environment. Radioengineering, 2018, vol. 27, no. 2, p. 557–571. DOI: 10.13164/re.2018.0557
  4. ROUABAH, K., ATTIA, S., FLISSI, M., et al. Efficient technique for DLL S‐curve side zero‐crossings cancellation in global positioning system/Galileo receiver. IET Signal Processing, 2019, vol. 13, no. 3, p. 338–347. DOI: 10.1049/iet-spr.2018.5379
  5. HUSSAIN, A., AHMED, A., MAGSI, H., et al. Adaptive GNSS receiver design for highly dynamic multipath environments. IEEE Access, 2020, vol. 8, p.
  6. 172481–172497. DOI: 10.1109/ACCESS.2020.3024890
  7. ROUABAH, K., CHIKOUCHE, D., BOUTTOUT, F., et al. GPS/Galileo multipath mitigation using the first side peak of double delta correlator. Wireless Personal Communications, 2011, vol. 60, no. 2, p. 321–333. DOI: 10.1007/s11277-010-9946-2
  8. GAO, G. X., SGAMMINI, M., LU, M., et al. Protecting GNSS receivers from jamming and interference. Proceedings of IEEE, 2016, vol. 104, no. 6, p.
  9. 1327–1338. DOI: 10.1109/JPROC.2016.2525938
  10. FLISSI, M., ROUABAH, K., ATTIA, S., et al. Consistent BCS modulated signals for GNSS applications. IET Signal Processing, 2017, vol. 11, no. 4, p. 415–421. DOI: 10.1049/iet-spr.2016.0200
  11. STRODE, P. R. R., GROVES, P. D. GNSS multipath detection using three-frequency signal-to-noise measurements. GPS Solution, 2016, vol. 20, p. 399–412. DOI: 10.1007/s10291-0150449-1
  12. SKLAR, J. R. Interference mitigation approaches for the global positioning system. Lincoln Laboratory Journal, 2003, vol. 14, no. 2, p. 167–179.
  13. FANTE, R., KUNYSZ, W., RAO, B. R. GPS/GNSS Antennas. Artech House, 2013. ISBN: 9781596931503
  14. FERNANDEZ-PRADES, C., ARRIBAS, J., CLOSAS, P. Robust GNSS receivers by array signal processing: Theory and implementation. Proceedings of IEEE, 2016, vol. 104, no. 6, p. 1207–1220. DOI: 10.1109/JPROC.2016.2532963
  15. DARDARI, D., FELLETTI, E., LUISE, M. (Eds.) Satellite and Terrestrial Radio Positioning Techniques: A Signal Processing Perspective. Academic Press, 2011. DOI: 10.1016/C2009-061856-0
  16. BORIO, D., GIOIA, C. GNSS interference mitigation: A measurement and position domain assessment. Navigation: Journal of the Institute of Navigation, 2021, vol. 68, no. 1, p. 93–114. DOI: 10.1002/navi.391
  17. VOLAKIS, J. L., O’BRIEN, A. J., CHEN, C. C. Small and adaptive antennas and arrays for GNSS applications. Proceedings of IEEE, 2016, vol. 104, no. 6, p. 1221–1232. DOI: 10.1109/JPROC.2016.2528165
  18. MAQSOOD, M., GAO, S., BROWN, T. W. C., et al. A compact multipath mitigating ground plane for multiband GNSS antennas. IEEE Transactions of. Antennas and Propagation, 2013, vol. 61, no. 5, p. 2775–2782. DOI: 10.1109/TAP.2013.2243692
  19. AMAMI, M. Testing patch, helix and vertical dipole GPS antennas with/without choke ring frame. International Journal for Research in Applied Science and Engineering Technology, 2022, vol. 10, no. 2, p. 933–938. DOI: 10.22214/ijraset.2022.40396
  20. CHEN, Z. N., CHIA, M. Y. W. Broadband Planar Antennas: Design and Applications. Wiley, 2006. DOI: 10.1002/0470871768
  21. GARCIA GAMEZ, L. Analysis and design of compact antennas in cavity based on metasurfaces for multiband GNSS applications. Theses. Universite Rennes 1, 2020.
  22. BOULARAS, A., ROUABAH, K., FLISSI, M. Miniaturized self complementary frequency selective surface for GNSS applications. In IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI). Singapore, 2021, p. 1707–1708. DOI: 10.1109/APS/URSI47566.2021.9704590
  23. ANWAR, R. S., MAO, L., NING, H. Frequency selective surfaces: A review. Applied Sciences, 2018, vol. 8, no. 9, p. 1–46. DOI: 10.3390/app8091689
  24. PANWAR, R., LEE, J. R. Progress in frequency selective surface based smart electromagnetic structures: A critical review. Aerospace Science and Technology, 2017, vol. 66, p. 216–234. DOI: 10.1016/j.ast.2017.03.006
  25. FAKHARIAN, M. M., REZAEI, P., OROUJI, A. A. Polarization and radiation pattern reconfigurability of a planar monopole-fed loop antenna for GPS application. Radioengineering, 2016, vol. 25, no. 4, p. 680–686. DOI: 10.13164/re.2016.0680
  26. SUN, Y. X., LEUNG, K. W., LU, K. Broadbeam cross-dipole antenna for GPS applications. IEEE Transactions of Antennas and Propagation, 2017, vol. 65, no. 10, p. 5605–5610. DOI: 10.1109/TAP.2017.2742585
  27. CAUSSE, A., RODRIGUEZ, K., BERNARD, L., et al. Compact bandwidth enhanced cavity-backed magneto-electric dipole antenna with outer Γ-shaped probe for GNSS bands. Sensors, 2021, vol. 21, no. 11, p. 1–11. DOI: 10.3390/s21113599
  28. CHEN, M., CHEN, C. C. A compact dual-band GPS antenna design. IEEE Antennas and Wireless Propagation Letters, 2013, vol. 12, p. 245–248. DOI: 10.1109/LAWP.2013.2247972
  29. AKMAL, M., ALIELDIN, A., ELDAMAK, A. R. A high-power sandwiched omnidirectional circularly polarized antenna for GNSS systems. IEEE Access, 2023, vol. 11, p. 31167–31176. DOI: 10.1109/ACCESS.2023.3262236
  30. KHOSRAVI, F., MOGHADAS, H., MOUSAVI, P. A GNSS antenna with a polarization selective surface for the mitigation of low-angle multipath interference. IEEE Transactions of Antennas and Propagation, 2015, vol. 63, no. 12, p. 5287–5295. DOI: 10.1109/TAP.2015.2491323
  31. TAHIR, F. A., ARSHAD, T., ULLAH, S., et al. A novel FSS for gain enhancement of printed antennas in UWB frequency spectrum. Microwave and Optical Technology Letters, 2017, vol. 59, no. 10, p. 2698–2704. DOI: 10.1002/mop.30789
  32. MONDAL, R., REDDY, P. S., SARKAR, D. C., et al. Compact ultra‐wideband antenna: Improvement of gain and FBR across the entire bandwidth using FSS. IET Microwaves, Antennas and Propagation, 2020, vol. 14, no. 1, p. 66–74. DOI: 10.1049/ietmap.2019.0536
  33. POZAR, D. M. Microwave Engineering. 4th ed. Wiley, 2011. P. 147–148. ISBN: 978-0-470-63155-3
  34. KUMAR, A., DE, A., JAIN, R. K. Gain enhancement using modified circular loop FSS loaded with slot antenna for sub-6 GHz 5G application. Progress in Electromagnetics Research Letters, 2021, vol. 98, p. 41–48. DOI: 10.2528/PIERL21031108
  35. SUPRIYA, A. S., RAJENDRAN, J. A low cost tri-band microstrip patch antenna for GPS application. In Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL). Singapore, 2017, p. 60–65. DOI: 10.1109/PIERS-FALL.2017.8293111
  36. BAGGEN, R., MARTINEZ-VAZQUEZ, M., LEISS, J., et al. Low profile GALILEO antenna using EBG technology. IEEE Transactions of Antennas and Propagation, 2008, vol. 56, no. 3, p. 667–674. DOI: 10.1109/TAP.2008.916927

Keywords: FSS, GNSS, GPS, slot antenna, high gain antenna, multiband

M. I. Al-Rayif [references] [full-text] [DOI: 10.13164/re.2024.0744] [Download Citations]
PAPR Reduction of OTSM with Random and Orthogonal SLM Phase Sequences and its Recovery in the Presence of EPA, EVA and ETU Channel Models

To overcome the high peak-to-average-power ratio (PAPR) in orthogonal time sequency multiplexing (OTSM), this document proposes orthogonal and random selective mapping phase vectors (OSLM and RSLM) after some modifications to be applicable with OTSM constructions at both transmitter and receiver. Consequently, the PAPR (without explicit side information) of the original phase vectors is reduced and restored in the presence of a nonlinear power amplifier, and three different multipath fading channel models: Extended pedestrian A (EPA), Extended vehicular A (EVA) and Extended typical urban (ETU), with different user speed, namely 150 and 500 km/h. Also, complementary cumulative distribution function (CCDF) PAPR comparisons are provided based on the Walsh Hadamard transform (WHT) matrix W_N and the inverse symplectic fast Fourier transform (ISFFT) matrix F_N. Furthermore, three detectors are modified and implemented in this work, namely single tap MMSE (ST-MMSE), Gauss-Seidel iterative matched filter (MFGS) and linear MMSE (LMMSE). As a result, this proposal shows reliable performance in terms of PAPR reduction, bit error rate (BER) and side information error rate (SIER) of the OTSM system at a fraction of extension values (f=0.09 and C=1.15) while maintaining power efficiency.

  1. HARMUTH, H. F. Applications of Walsh functions in communications. Spectrum, 1969, vol. 6, no. 11, p. 82–91. DOI:10.1109/MSPEC.1969.5214175
  2. THAJ, T., VITERBO, E., HONG, Y. Orthogonal time sequency multiplexing modulation: Analysis and low-complexity receiver design. Transactions on Wireless Communications, 2021, vol. 20, no. 12, p. 7842–7855. DOI: 10.1109/TWC.2021.3088479
  3. THAJ, T., VITERBO, E. Orthogonal time sequency multiplexing modulation. In IEEE Wireless Communications and Networking Conference (WCNC). Nanjing (China), 2021, p. 1–7. DOI: 10.1109/WCNC49053.2021.9417451
  4. HARMUTH, H. F. Delay-Doppler Communications Principles and Applications. 1st ed. Academic Press, 2022. ISBN: 9780323850285
  5. MOHAMMED, S.K. Derivation of OTFS modulation from first principles. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 8, p. 7619–7636. DOI: 10.1109/TVT.2021.3069913
  6. REDDY, B., VELAMPALLI, C., DAS, S. Performance analysis of multi-user OTFS, OTSM, and single carrier in uplink. IEEE Transactions on Communications, 2024, vol. 72, no. 3, p. 1428–1443. DOI: 10.1109/TCOMM.2023.3332865
  7. KUMAR, A., SHARMA, H., GAUR, N., et al. PAPR analysis in OTFS using the centre phase sequence matrix based PTS method. Results in Optics, 2024, vol. 15, p. 1–8. DOI: 10.1016/J.RIO.2024.100664
  8. SURABHI, G. D., AUGUSTINE, R. M., CHOCKALINGAM, A. Peak-to-average power ratio of OTFS modulation. IEEE Communications Letters, 2019, vol. 23, no. 6, p. 999–1002. DOI: 10.1109/LCOMM.2019.2914042
  9. DOOSTI-AREF, A., MASOUROS, C., BASAR, E., et al. Pairwise sequency index modulation with OTSM for Green and robust single-carrier communications. IEEE Wireless Communications Letters, 2024, vol. 13, no. 4, p. 1083–1087. DOI: 10.1109/LWC.2024.3360256
  10. NAVEEN, C., SUDHA, V. Peak-to-average power ratio reduction in OTFS modulation using companding technique. In The 5th International Conference on Devices, Circuits and Systems (ICDCS). Coimbatore (India), 2020, p. 140–143. DOI: 10.1109/ICDCS48716.2020.243567
  11. AL AHSAN, R., GHANNOUCHI, F. M., FAPOJUWO, A. O. Analysis of adapted tone reservation PAPR reduction techniques in OTSM system. In The 33rd International Telecommunication Networks and Applications Conference. Melbourne (Australia), 2023, p. 162–168. DOI: 10.1109/ITNAC59571.2023.10368554
  12. NEELAM, S. G., SAHU, P. R. Iterative channel estimation and data detection of OTSM with superimposed pilot scheme and PAPR analysis. IEEE Communications Letters, 2023, vol. 27, no. 8, p. 2147–2151. DOI: 10.1109/LCOMM.2023.3281575
  13. KALPAGE, N. V., PRIYA, P., HONG, Y. DCT-based OTFS with reduced PAPR. Communications Letters, 2024, vol. 28, no. 1, p. 158–162. DOI: 10.1109/LCOMM.2023.3337778
  14. WANG, Z., CHEN, X., SUN, Z., et al. On the performance of nonlinear corrective active constellation expansion in OTFS systems. IEEE Communications Letters, 2022, vol. 26, no. 6, p. 1418–1422. DOI: 10.1109/LCOMM.2022.3165191
  15. SHANG, H., CHEN, R., ZHANG, H., et al. OTFS modulation and PAPR reduction for IoT-railways. China Communications, 2023, vol. 20, no. 1, p. 102–113. DOI: 10.23919/JCC.2023.01.009
  16. GAO, S., ZHENG, J. Peak-to-average power ratio reduction in pilot embedded OTFS modulation through iterative clipping and filtering. IEEE Communications Letters, 2020, vol. 24, no. 9, p. 2055–2059. DOI: 10.1109/LCOMM.2020.2993036
  17. TEK, Y.I., BASAR, E. PAPR reduction precoding for orthogonal time frequency space modulation. In The 46th International Conference on Telecommunications and Signal Processing (TSP). Prague(Czech Republic), 2023, p. 172–176. DOI: 10.1109/TSP59544.2023.10197718
  18. SU,J., LIU, S., HUANG,Y., et al. Peak-to-average power ratio reduction via symbol precoding in OTFS modulation. In IEEE 95th Vehicular Technology Conference (VTC2022-Spring). Helsinki (Finland), 2022, p. 1–5. DOI: 10.1109/VTC2022-Spring54318.2022.9860629
  19. BAENA-LECUYER, V., ORIA ORIA, A. C., GRANADO ROMERO, J. Low PAPR preamble-based channel estimation for OTFS systems on static multipath channels. Digital Signal Processing, 2023, vol. 136, p. 1–7. DOI: 10.1016/J.DSP.2023.103979
  20. BAI, J., LIN, B., TAO, X. Improved PAPR reduction algorithm for OTFS systems. In The 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). Harbin (China), 2023, p. 1–6. DOI: 10.1109/ICNC-FSKD59587.2023.10281178
  21. CHENNAMSETTY, S., BODDU, S., CHANDHAR, P. , et al. Analysis of PAPR in OTFS modulation with classical selected mapping technique. In The 15th International Conference on COMmunication Systems and Networks (COMSNETS). Bangalore (India), 2023, p. 319–322. DOI: 10.1109/COMSNETS56262.2023.10041313
  22. SHARMA, S., SHAH, S. W. H., WIDMER, J. A low complexity standard-compliant PAPR reduction scheme for OTFS modulation. In IEEE 99th Vehicular Technology Conference (VTC2024-Spring). Singapore, 2024, p. 1–7. DOI: 10.1109/VTC2024-Spring62846.2024.10683635
  23. ASSERI, M. I., TSIMENIDIS, C. C., SHARIF, B. S., et al. Orthogonal-SLM technique for PAPR reduction and recovery without side information in OFDM systems. In The 7th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). Newcastle upon Tyne (UK), 2010, p. 162–166. DOI: 10.1109/CSNDSP16145.2010.5580440
  24. LE GOFF, S. Y., KHOO, B. K., TSIMENIDIS, C. C., et al. A novel selected mapping technique for PAPR reduction in OFDM systems. IEEE Transactions on Communications, 2008, vol. 56, no. 11, p. 1775–1779. DOI: 10.1109/TCOMM.2008.070021
  25. THAJ, T., VITERBO, E. Low complexity iterative rake decision feedback equalizer for zero-padded OTFS systems. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 12, p. 15606–15622. DOI: 10.1109/TVT.2020.3044276
  26. BJORCK, A. Numerical Methods for Least Squares Problems. SIAM, 1996. DOI: 10.1137/1.9781611971484
  27. DUDAK, C., KOC, A. T., KOC, S. Solid state power amplifier (SSPA) nonlinearity effects on quadri-phase shift keying modulation. In The 7th European Conference on Wireless Technology. Amsterdam (Netherlands), 2004, p. 237–240. ISBN: 1580539912
  28. STRAUSS, R. Reliability of SSPA’s and TWTA’s. IEEE Transactions on Electron Devices, 1994, vol. 41, no. 4, p. 625–626. DOI: 10.1109/16.278524

Keywords: PAPR, SLM, OTFS, OTSM, LMMSE, single-tap MMSE, Gauss-Seidel iterative matched filter

A. Pham, D. Bui, P. T. P. Nguyen, L. Tran [references] [full-text] [DOI: 10.13164/re.2024.0758] [Download Citations]
Innovative TCAM Solutions for IPv6 Lookup: Don't Care Reduction and Data Relocation Techniques

Ternary Content-Addressable Memory (TCAM) enables high-speed searches by comparing search data with all stored data in a single clock cycle, using ternary logic ("0", "1", "X" for "don't care") for flexible matching. This makes TCAM ideal for applications like network routers and lookup tables. However, TCAM's speed increases silicon area and limits memory capacity. This paper introduces a low-area, enhanced-capacity TCAM for IPv6 lookup tables using Don't Care Reduction (DCR) and Data Relocation (DR) techniques. The DCR technique requires only (N + log_2(N))-bit memory for an N-bit IP address, reducing the need for 2N-bit memory. The DR technique improves TCAM storage capabilities by classifying the IPv6 into 4 different prefix length types and relocating the data in the prefix bit into the "X" cells. The design features a 256x128-bit TCAM (eight 32x128-bit memory banks) on a 65 nm process with a 1.2 V operation voltage. Results show a 71.47% increase in area efficiency per stored IP value compared to conventional TCAM and a 20.97% increase compared to data-relocation TCAM.

  1. AKHBARIZADEH, M. J., NOURANI, M. Hardware-based IP routing using partitioned lookup table. IEEE/ACM Transactions on Networking, 2005, vol. 13, no. 4, p. 769–781. DOI: 10.1109/TNET.2005.852885
  2. PAGIAMTZIS, K., SHEIKHOLESLAMI, A. Content-addressable memory (CAM) circuits and architectures: A tutorial and survey. IEEE Journal of Solid-State Circuits, 2006, vol.41, no.3, p. 712–727. DOI: 10.1109/JSSC.2005.864128
  3. YANG, B.-D. Low-power effective memory-size expanded TCAM using data-relocation scheme. IEEE Journal of Solid State Circuits, 2015, vol. 50, no. 10, p. 2441–2450. DOI: 10.1109/JSSC.2015.2457908
  4. WOO, K.-C., YANG, B.-D. Low-area TCAM using a Don’t Care reduction scheme. IEEE Journal of Solid-State Circuits, 2018, vol. 53, no. 8, p. 2427–2433. DOI: 10.1109/JSSC.2018.2822696
  5. ARSOVSKI, I., HEBIG, T., DOBSON, D., et al. A 32nm 0.58 fJ/bit/search 1-GHz ternary content addressable memory compiler using silicon-aware early-predict late-correct sensing with embedded deep-trench capacitor noise mitigation. IEEE Journal of Solid-State Circuits, 2013, vol. 48, no. 4, p. 932–939. DOI: 10.1109/JSSC.2013.2239092
  6. HUANG, P.-T., HWANG, W. A 65nm 0.165 fJ/bit/search 256 × 144 TCAM macro design for IPv6 lookup tables. IEEE Journal of Solid-State Circuits, 2011, vol. 46, no. 2, p. 507–519. DOI: 10.1109/JSSC.2010.2082270
  7. MIYATAKE, H., TANAKA, M., MORI, Y. A design for high-speed low-power CMOS fully parallel content - addressable memory macros. IEEE Journal of Solid-State Circuits, 2001, vol.36, no.6, p.956–968. DOI: 10.1109/4.924858
  8. YANG, B.-D., LEE, Y.-K. A low-power CAM using pulsed NAND NOR match-line and charge-recycling search-line driver. IEEE Journal of Solid-State Circuits, 2005, vol. 40, no. 8, p. 1736–1744. DOI: 10.1109/JSSC.2005.852028
  9. YANG, B.-D., LEE, Y.-K., SUNG, S.-W., et al. A low power content addressable memory using low swing search lines. IEEE Transactions on Circuits and Systems I: Regular Papers, 2011, vol. 58, no. 12, p. 2849–2858. DOI: 10.1109/TCSI.2011.2158703
  10. WANG, C.-C., CHENG, C.-J., CHEN, T.-F., et al. An adaptively dividable dual-port BiTCAM for virus-detection processors in mobile devices. In IEEE International Solid-State Circuits Conference– Digest of Technical Papers. San Francisco (CA, USA), 2008, p. 390–622. DOI: 0.1109/ISSCC.2008.4523221
  11. ARSOVSKI, I., CHANDLER, T., SHEIKHOLESLAMI, A. A ternary content-addressable memory (TCAM) based on 4T static storage and including a current-race sensing scheme. IEEE Journal of Solid-State Circuits, 2003, vol. 38, no. 1, p. 155–158. DOI: 10.1109/JSSC.2002.806264
  12. ARSOVSKI, I., CHANDLER, T., SHEIKHOLESLAMI, A. A mismatch-dependent power allocation technique for match line sensing in content-addressable memories. IEEE Journal of Solid-State Circuits, 2003, vol. 38, no. 11, p. 1958–1966. DOI: 10.1109/JSSC.2003.818139
  13. MOHAN, N., SACHDEV, M. Low-capacitance and charge-shared match lines for low-energy high-performance TCAMs. IEEE Journal of Solid-State Circuits, 2007, vol. 42, no. 9, p. 2054–2060. DOI: 10.1109/JSSC.2007.903089
  14. BAEG, S. Low-power ternary content-addressable memory design using a segmented match line. IEEE Transactions on Circuits and Systems I: Regular Papers, 2008, vol. 55, no. 6, p. 1485–1494. DOI: 10.1109/TCSI.2008.916624
  15. LIN, C.-S., CHANG, J.-C., LIU, B.-D. A low-power precomputation based fully parallel content-addressable memory. IEEE Journal of Solid-State Circuits, 2003, vol. 38, no. 4, p. 654–662. DOI: 10.1109/JSSC.2003.809515
  16. PAGIAMTZIS, K., SHEIKHOLESLAMI, A. A low-power content addressable memory (CAM) using pipelined hierarchical search scheme. IEEE Journal of Solid-State Circuits, 2004, vol. 39, no. 9, p. 1512–1519. DOI: 10.1109/JSSC.2004.831433
  17. CHANG, Y.-J. A high-performance and energy-efficient TCAM design for IP-address lookup. IEEE Transactions on Circuits and Systems II: Express Briefs, 2009, vol. 56, no. 6, p. 479–483. DOI: 10.1109/TCSII.2009.2020935
  18. LI, H.-Y., CHEN, C.-C., WANG, J.-S., et al. An AND-type match line scheme for high-performance energy-efficient content addressable memories. IEEE Journal of Solid-State Circuits, 2006, vol. 41, no. 5, p. 1108–1119. DOI: 10.1109/JSSC.2006.872719
  19. KADIR, R. B., ANWAR, A. S., MUNTASIM-UL-HAQUE. Analysis of charge-shared match line sensing schemes and current race scheme in high-speed ternary content addressable memory (TCAM). In International Conference on Innovations in Science, Engineering and Technology (ICISET). Dhaka (Bangladesh), 2016, p. 1–4. DOI: 10.1109/ICISET.2016.7856490
  20. SHAFAI, F., SCHULTZ, K.J., GIBSON, G.F.R., et al. Fully parallel 30-MHz, 2.5-Mb CAM. IEEE Journal of Solid-State Circuits, 1998, vol. 33, no. 11, p. 1690–1696. DOI: 10.1109/4.726560
  21. HANZAWA, S., SAKATA, T., KAJIGAYA, K., et al. A large scale and low-power CAM architecture featuring a one-hot-spot block code for IP-address lookup in a network router. IEEE Journal of Solid-State Circuits, 2005, vol. 40, no. 4, p. 853–861. DOI: 10.1109/JSSC.2005.845554
  22. HAYASHI, I., AMANO,T., WATANABE,N., et al. A 250-MHz 18 Mb full ternary CAM with low-voltage match line sensing scheme in 65-nm CMOS. IEEE Journal of Solid-State Circuits, 2013, vol. 48, no. 11, p. 2671–2680. DOI: 10.1109/JSSC.2013.2274888
  23. WANG, C.-C., WANG, J.-S., YEH, C. High-speed and low power design techniques for TCAM macros. IEEE Journal of Solid-State Circuits, 2008, vol. 43, no. 2, p. 530–540. DOI: 10.1109/JSSC.2007.914330
  24. SALEH, S., GOOSSENS, A. H., BANERJEE, T., et al. TCAmM CogniGron: Energy efficient memristor-based TCAM for match action processing. In IEEE International Conference on Rebooting Computing (ICRC). San Francisco (CA, USA), 2022, p. 89–99. DOI: 10.1109/ICRC57508.2022.00013
  25. CHOWDHURY, Z. I., RESCH, S., CILASUN, H., et al. CAMeleon: Reconfigurable B(T)CAM in computational RAM. In Proceedings of the Great Lakes Symposium on VLSI (GLSVLSI). Virtual Event, 2021, p. 57–63. DOI: 10.1145/3453688.3461507
  26. KHAN, M. R., RASHID, A. H. Memristor-transistor hybrid ternary content addressable memory using ternary memristive memory cell. IET Circuits, Devices & Systems, 2021, vol. 15, no. 7, p. 619–629. DOI: 10.1049/CDS2.12057
  27. KIM, S. M., KIM, K. M., CHOI, J. H., et al. A digital processing in memory architecture using TCAM for rapid learning and inference based on a spike location dependent plasticity. IEEE Access, 2023, vol. 11, p. 3416–3430. DOI: 10.1109/ACCESS.2023.3234323

Keywords: Ternary content-addressable memory (TCAM), high-speed searches, IP version 6 (IPv6), IPv6 lookup table, don't care reduction (DCR), data relocation (DR), conventional TCAM (CV-TCAM), data-relocation TCAM (DR-TCAM), low-area technique, enhanced-capacity technique