R. Cheng, J. Zhang, J. Deng, Y. Zhu [references] [full-text] [DOI: 10.13164/re.2023.0469] [Download Citations]
Lightweight Spectrum Prediction Based on Knowledge Distillation

To address the challenges of increasing complexity and larger number of training samples required for high-accuracy spectrum prediction, we propose a novel lightweight model, leveraging a temporal convolutional network (TCN) and knowledge distillation. First, the prediction accuracy of TCN is enhanced via a self-transfer method. Then, we design a two-branch network which can extract the spectrum features efficiently. By employing knowledge distillation, we transfer the knowledge from TCN to the two-branch network, resulting in improved accuracy for spectrum prediction of the lightweight network. Experimental results show that the proposed model can improve accuracy by 19.5% compared to the widely-used LSTM model with sufficient historical data and reduces 71.1% parameters to be trained. Furthermore, the prediction accuracy is improved by 17.9% compared to Gated Recurrent Units (GRU) in the scenarios with scarce historical data.

1. ELTHOLTH, A. Forward backward autoregressive spectrum prediction scheme. In 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS). Cairns (Australia), 2015, p. 1–5. DOI: 10.1109/icspcs.2015.7391770
2. MOSAVAT-JAHROMI, H., LI, Y., CAI, L., et al. Prediction and modeling of spectrum occupancy for dynamic spectrum access systems. IEEE Transactions on Cognitive Communications and Networking, 2021, vol. 7, no. 3, p. 715–728. DOI: 10.1109/tccn.2020.3048105
3. CHEN, X., YANG, J., DING, G. Minimum Bayesian risk based robust spectrum prediction in the presence of sensing errors. IEEE Access, 2010, vol. 6, p. 29611–29625. DOI: 10.1109/ACCESS.2018.2836940
4. YIN, L., YIN, S., HONG, W., et al. Spectrum behavior learning in cognitive radio based on artificial neural network. In 2011-MILCOM 2011 Military Communications Conference. Baltimore (USA), 2011, p. 25–30. DOI: 10.1109/MILCOM.2011.6127671
5. WANG, X., PENG, T., ZUO, P., et al. Spectrum prediction method for ISM bands based on LSTM. In 2020 5th International Conference on Computer and Communication Systems (ICCCS). Shanghai (China), 2020, p. 580–584. DOI: 10.1109/ICCCS49078.2020.9118535
6. MIAO, J., LI, Y., JING, X., et al. Spectrum sensing based on adversarial transfer learning. IET Communications, 2020, vol. 16, no. 17, p. 2059–2069. DOI: 10.1049/cmu2.12459
7. DING, G., WANG, J., WU, Q., et al. Joint spectral-temporal spectrum prediction from incomplete historical observations. In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Atlanta (USA), 2014, p. 1325–1329. DOI: 10.1109/GlobalSIP.2014.7032338
8. DING, G., ZHAI, S., CHEN, X., et al. Robust spectral-temporal two-dimensional spectrum prediction. In Machine Learning and Intelligent Communications: First International Conference. MLICOM. Shanghai (China), 2017, p. 393–401. DOI: 10.1007/978-3-319-52730-7_40
9. DING, G., WU, F., WU, Q., et al. Robust online spectrum prediction with incomplete and corrupted historical observations. IEEE Transactions on Vehicular Technology, 2017, vol. 66, no. 9, p. 8022–8036. DOI: 10.1109/TVT.2017.2693384
10. LIN, F., CHEN, J., SUN, J., et al. Cross-band spectrum prediction based on deep transfer learning. China Communications, 2020, vol. 17, no. 2, p. 66–80. DOI: 10.23919/JCC.2020.02.006
11. LIN, F., CHEN, J., DING, G., et al. Spectrum prediction based on GAN and deep transfer learning: A cross-band data augmentation framework. China Communications, 2021, vol. 18, no. 1, p. 18–32. DOI: 10.23919/JCC.2021.01.002
12. PENG, C., ZHANG, M., HU, W., et al. Cross-band spectrum prediction algorithm based on Transfer Learning and Meta Learning. In 7th International Conference on Computer and Communications (ICCC). Chengdu (China), 2021, p. 2303–2307. DOI: 10.1109/ICCC54389.2021.9674444
13. HINTON, G., VINYALS, O., DEAN, J. Distilling the knowledge in a neural network. Computer Science, 2015, vol. 14, no. 7, p. 38 to 49. DOI: 10.48550/arXiv.1503.02531
14. SHAO, R., LIU, Y., ZHANG, W., et al. A survey of knowledge distillation on deep learning (in Chinese). Chinese Journal of Computers, 2020, vol. 45, no. 8, p. 1638–1673. DOI: 10.11897/SP.J.1016.2022.01638
15. ZHAI, N., ZHOU, X., LI, S., et al. Prediction method of furnace temperature based on transfer learning and knowledge distillation (in Chinese). Computer Integrated Manufacturing Systems, 2022, vol. 28, no. 6, p. 1860–1869. DOI: 10.13196/j.cims.2022.06.024
16. ZHANG, X., LIU, Y. Remaining useful life prediction of aero-engine based on knowledge distillation compression hybrid model (in Chinese). 15 pages. [Online] Cited at 2022-10-12. Available at: http://kns.cnki.net/kcms/detail/11.5946.TP.20221011.1557.024.html
17. AY, E., DEVANNE, M., WEBER, J., et al. A study of knowledge distillation in fully convolutional network for time series classification. In 2022 International Joint Conference on Neural Networks (IJCNN). Padua (Italy), 2022, p. 1–8. DOI: 10.1109/IJCNN55064.2022.9892915
18. ZHANG, Y., HU, G., CAI, Y. Proactive spectrum monitoring with spectrum monitoring data transmission in dynamic spectrum sharing network: Joint design of precoding and antenna selection. IET Communications, 2021, vol. 15, no. 18, p. 2265–2274. DOI: 10.1049/cmu2.12260
19. YU, L., CHEN, J., DING, G. Spectrum prediction via long short term memory. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). Chengdu (China), 2017, p. 643–647. DOI: 10.1109/COMPCOMM.2017.8322623
20. ZHANG, T., ZHANG, Y., CAO, W., et al. Less is more: Fast multivariate time series forecasting with light sampling-oriented MLP structures. Computer Science, 2022, p. 1–11. DOI: 10.48550/arXiv.2207.01186
21. HARELL, A., MAKONIN, S., BAJIC, I. A causal neural network for power disaggregation from the complex power signal. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton (UK), 2019, p. 8335–8339. DOI: 10.1109/ICASSP.2019.8682543
22. CHANG, S., LI, B., SIMKO, G., et al. Temporal modeling using dilated convolution and gating for voice-activity-detection. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary (Canada), 2018, p. 5549 to 5553. DOI: 10.1109/ICASSP.2018.8461921
23. WELLENS, M. Empirical Modelling of Spectrum Use and Evaluation of Adaptive Spectrum Sensing in Dynamic Spectrum Access Networks. [Online] Cited 2010-05-14. Available at: https://publications.rwth-aachen.de/record/51779/files/3248.pdf
24. LI, X., LIU, Z., CHEN, G., et al. Deep learning for spectrum prediction from spatial-temporal-spectral data. IEEE Communications Letters, 2021, vol. 25, no. 4, p. 1216–1220. DOI: 10.1109/LCOMM.2020.3045205
25. SUN, J., SHEN, L., DING, G., et al. Predictability analysis of spectrum state evolution: Performance bounds and real-world data analytics. IEEE Access, 2017, vol. 5, no. 10, p. 22760–22774. DOI: 10.1109/ACCESS.2017.2766076
26. LAZCANO, A., HERRERA, P., MONGE, M. A combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting. Mathematics, 2023, vol. 11, no. 1, p. 1–21. DOI: 10.3390/math11010224
27. YIM, J., JOO, D., BAE, J., et al. A gift from knowledge distilla-tion: Fast optimization, network minimization and transfer learn-ing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Honolulu (USA), 2017, p. 4133–4141. DOI: 10.1109/CVPR.2017.754

Keywords: Spectrum prediction, knowledge distillation, temporal convolutional network, lightweight networks, few-shot learning

B. Jovanovic, S. Milenkovic [references] [full-text] [DOI: 10.13164/re.2023.0479] [Download Citations]
IQ Imbalance Correction in Wideband Software Defined Radio Transceivers

A method for compensation of frequency-selective (FS) in-phase/quadrature (IQ) imbalance of a wideband transceiver is proposed in the paper. It is dedicated for implementation in software defined radio (SDR) cellular base stations. Both transmitter (TX) and receiver (RX) IQ impairments are corrected by complex valued finite impulse response (FIR) filters which are designed based on previously found imbalance correction models. The compensation performance is assessed after the method was implemented in the SDR platform capable of transmitting signals at different central frequencies. At frequencies higher than 3 GHz measured IQ gain and phase error functions exhibit asymmetrical characteristic. In order to reduce the level of asymmetry, adopted IQ gain correction model incorporates odd polynomial elements while the phase correction model includes even polynomial parts. Regardless of utilized central frequency IQ impairments are efficiently compensated. The advantage of the proposed method is low complexity. The method doesn't require specialized hardware for calibration, instead, it uses the RF loopback. At central frequency of 3.5 GHz, transmitter image rejection ratio (IRR) is increased from 20 dBc to 45-50 dBc by applying the proposed method. After receiver imbalance is compensated, the improvement in IRR of more than 25 dBc is achieved.

1. CAVERS, J. K., LIAO, M. W. Adaptive compensation for imbalance and offset losses in direct conversion transceivers. IEEE Transactions on Vehicular Technology, 1993, vol. 42, no. 4, p. 581–588. DOI: 10.1109/25.260752
2. RAZAVI, B. Design considerations for direct-conversion receivers. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 1997, vol. 44, no. 6, p. 428–435. DOI: 10.1109/82.592569
3. VALKAMA, M. Advanced I/Q Signal Processing for Wideband Receivers: Models and Algorithms. Ph.D. dissertation. Tampere (Finland): Tampere University of Technology, 2001. ISBN: 9521507152
4. 3GPP. Technical Specification Group Radio Access Network; NR; Base Station (BS) Radio Transmission and Reception. 2019, Valbonne, France, Tech. Rep.
5. KISS, P., PRODANOV, V. One-tap wideband I/Q compensation for zero-IF filters. IEEE Transactions on Circuits and Systems I: Regular Papers, 2004, vol. 51, no. 6, p. 1062–1074. DOI: 10.1109/TCSI.2004.829233
6. LIM, A. G. K. C., SREERAM, V., WANG, G. Q. Digital compensation in IQ modulators using adaptive FIR filters. IEEE Transactions on Vehicular Technology, 2004, vol. 53, no. 6, p. 1809–1817. DOI: 10.1109/TVT.2004.836934
7. TUTHILL, J. CANTONI, A. Efficient compensation for frequency-dependent errors in analog reconstruction filters used in IQ modulators. IEEE Transactions on Communications, 2005, vol. 53, no. 3, p. 489–496. DOI: 10.1109/TCOMM.2005.843455
8. DING, L., MA, Z., MORGAN, D.R., et al. Compensation of frequency-dependent gain/phase imbalance in predistortion linearization systems. IEEE Transactions on Circuits and Systems I: Regular Papers, 2008, vol. 55, no. 1, p. 390–397. DOI: 10.1109/TCSI.2007.910545
9. ANTTILA, L., VALKAMA, M. RENFORS, M. Frequencyselective IQ mismatch calibration of wideband direct-conversion transmitters. IEEE Transactions on Circuits and Systems II: Express Briefs, 2008, vol. 55, no. 4, p. 359–363. DOI: 10.1109/TCSII.2008.919500
10. CAVERS, J. K. The effect of quadrature modulator and demodulator errors on adaptive digital predistorters for amplifier linearization. IEEE Transactions on Vehicular Technology, 1997, vol. 46, no. 2, p. 456–466. DOI: 10.1109/25.580784
11. ANTTILA, L., HANDEL, P., VALKAMA, M. Joint mitigation of power amplifier and I/Q modulator impairments in broadband direct-conversion transmitters. IEEE Transactions on Microwave Theory and Techniques, 2010, vol. 58, no. 4, p. 730–739. DOI: 10.1109/TMTT.2010.2041579
12. CAO, H., SOLTANI TEHRANI, A., FAGER, C., et al. I/Q imbalance compensation using a nonlinear modeling approach. IEEE Transactions on Microwave Theory and Techniques, 2009, vol. 57, no. 3, p. 513–518. DOI: 10.1109/TMTT.2008.2012305
13. LI, W., ZHANG, Y., HUANG, L. K., et al. Self-IQ-demodulation based compensation scheme of frequency-dependent IQ imbalance for wideband direct-conversion transmitters. IEEE Transactions on Broadcasting, 2015, vol. 61, no. 4, p. 666–673. DOI: 10.1109/TBC.2015.2465138
14. MKADEM, F., FARRES, M. C., BOUMAIZA, S., et al. Complexity reduced Volterra series model for power amplifier digital predistortion. Analog Integrated Circuits and Signal Processing, 2014, vol. 79, p. 331–343. DOI: 10.1007/s10470-014-0266-4
15. VALKAMA, M., RENFORS, M., KOIVUNEN, V. Blind source separation based I/Q imbalance compensation. In Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium. Lake Louise (Canada), 2000, p. 310–314. DOI: 10.1109/ASSPCC.2000.882491
16. CETIN, E., KALE, I., MORLING, R. Adaptive compensation of analog front-end I/Q mismatches in digital receivers. In Proceedings of 2001 IEEE International Symposium on Circuits and Systems (ISCAS 2001). Sydney (Australia), 2001, vol. 4, p. 370–373. DOI: 10.1109/ISCAS.2001.922250
17. ZEKKARI, C., DJENDI, M., GUESSOUM, A. Efficient adaptive filtering algorithm for IQ imbalance compensation TX/RX systems. IET Signal Processing, 2018, vol. 12, no. 5, p. 566–573. DOI: 10.1049/iet-spr.2017.0448
18. PENG, X., WANG, Z., MO, J., et al. A blind calibration model for I/Q imbalances of wideband zero-IF receivers. Electronics, 2020, vol. 9, no. 11, p. 1–16. DOI: 10.3390/electronics9111868
19. LI, Y., CHEANG, C. F., MAK, P. I., et al. Joint-digitalpredistortion for wireless transmitter’s I/Q imbalance and PA nonlinearities using an asymmetrical complexity-reduced Volterra series model. Analog Integrated Circuits and Signal Processing, 2016, vol. 87, no. 1, p. 35–47. DOI: 10.1007/s10470-016-0724-2
20. ROSOLOWSKI, D. W., KORPAS, P. IQ-imbalance and DC-offset compensation in ultra wideband zero-IF receiver. In Proceedings of the 23rd International Microwave and Radar Conference (MIKON). Warsaw (Poland), 2020, p. 209–213. DOI: 10.23919/MIKON48703.2020.9253894
21. MENG, J., WANG, H., YE, P., et al. I/Q linear phase imbalance estimation technique of the wideband zero-IF receiver. Electronics. 2020, vol. 9, no. 11, p. 1–14. DOI: 10.3390/electronics9111787
22. JOVANOVIĆ, B., MILENKOVIĆ, 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
23. INGLE, V. K., PROAKIS, J. G. Digital Signal Processing Using MATLAB: A Problem Solving Companion. 4th ed. Boston (USA): Cengage Learning, 2015. ISBN: 978-1305635128
24. FLETCHER, R. Practical Methods of Optimization. 2nd ed. New York (USA): John Wiley & Sons, 1987. ISBN: 978-0-471-91547-8
25. LIMEMICROSYSTEMS, UK. LimeSDR qPCIe (datasheet). [Online] Cited 2023-06-28. Available at: https://wiki.myriadrf.org/LimeSDR-QPCIe
26. KIELA, K., JURGO, M., MACAITIS, V., et al. R. 5G standalone and 4G multi-carrier network-in-a-box using a software defined radio framework. Sensors, 2021, no. 21, p. 1–18. DOI: 10.3390/s21165653
27. JOVANOVIĆ, B., MILENKOVIĆ, S. PA linearization by digital predistortion and peak to average power ratio reduction in software defined radios. Journal of Circuits, Systems and Computers, 2020, vol. 29, no. 9, p. 1–19. DOI: 10.1142/S0218126620501479
28. FAN, L., LI, Y. ZHAO, M. Joint IQ imbalance and PA nonlinearity pre-distortion for highly integrated millimetre-wave transmitters. In Proceedings of 2014 IEEE Globecom Workshops (GC Wkshps). Austin (USA), 2014, p. 399–404. DOI: 10.1109/GLOCOMW.2014.7063464
29. KIM, M., MARUICHI, Y., TAKADA, J. I. Parametric method of frequency dependent I/Q imbalance compensation for wideband quadrature modulator. IEEE Transactions on Microwave Theory and Techniques, 2013, vol. 61, no. 1, p. 270–280. DOI: 10.1109/TMTT.2012.2228215

Keywords: Frequency selective IQ imbalance, transmitter, receiver, software defined radio, IQ calibration

F. Titel, M. Belattar [references] [full-text] [DOI: 10.13164/re.2023.0492] [Download Citations]
Optimization of NOMA Downlink Network Parameters under Harvesting Energy Strategy Using Multi-Objective GWO

Non-orthogonal multiple access technique (NOMA) is based on the principle of sharing the same physical resource, over several power levels, where user’s signals are transmitted by using the superposition-coding scheme at the transmitter and these users signals are decoded by the receiver by means of successive interference cancellation technique (SIC). In this work, performance of NOMA Downlink network under Rayleigh fading distribution is studied, in the power domain where a power beacon (PB) is used to help a base station (BS) to serve distant users, by Wireless Power Transfer (WPT). The harvested energy permits by the BS, supports information signal transmission to NOMA users. This concept can be an effective way to power Internet of Things (IoT) devices, reduce battery dependency, and promote energy sustainability and may be used in SWIPT systems and vehicular networks. To improve the key performance indicators of the system expressed by the outage performance of NOMA users and system throughput, a Multi-Objective Grey Wolf Optimizer algorithm (MOGWO) is used to find optimal values of several influencing parameters. These parameters are partition time expressing the harvesting energy time, the power conversion factor and power allocation coefficients.

1. HASHEMI, R., BEYRANVAND, H., MILI, M. R., et al. Energy efficiency maximization in the uplink delta-OMA networks. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 9, p. 9566–9571. DOI: 10.1109/TVT.2021.3097128
2. WEI, Z., YANG, L., KWAN NG, D. W., et al. On the performance gain of NOMA over OMA in uplink communication systems. IEEE Transactions on Communications, 2020, vol. 68, no. 1, p. 536–568. DOI: 10.1109/TCOMM.2019.2948343
3. ZENG, M., YADAV, DOBRE, O. A., et al. Energy-efficient joint user-RB association and power allocation for uplink hybrid NOMA-OMA. IEEE Internet of Things Journal, 2019, vol. 6, no. 3, p. 5119–5131. DOI: 10.1109/JIOT.2019.2896946
4. AL-ERYANI, Y., HOSSAIN, E. The D-OMA method for massive multiple access in 6G: Performance, security, and challenges. IEEE Vehicular Technology Magazine, 2019, vol. 14, no. 3, p. 92 to 99. DOI: 10.1109/MVT.2019.2919279
5. TOMIDA, S., HIGUCHI, K. Non-orthogonal access with SIC in cellular downlink for user fairness enhancement. In 19th IEEE International Symposium on Intelligent Signal Processing. Chiang Mai (Thailand), 2011, p. 1–6. DOI: 10.1109/ISPACS.2011.6146188
6. UMEHARA, J., KISHIYAMA, Y., HIGUCHI, K. Enhancing user fairness in non-orthogonal access with successive interference cancellation for cellular downlink. In International Conference on Communication Systems. Singapore, 2012, p. 324–328, DOI: 10.1109/ICCS.2012.6406163
7. HIGUCHI, K., BENJEBBOUR, A. Non-orthogonal multiple access (NOMA) with successive interference cancellation. IEICE Transactions on Communications, 2015, vol. E98-B, no. 3, p. 403 to 414. DOI: 10.1587/transcom.E98.B.403
8. SUN, Y., DING, Z., DAI, X. A new design of hybrid SIC for improving transmission robustness in uplink NOMA. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 5, p. 5083–5087. DOI: 10.1109/TVT.2021.3069573
9. LIU, Y., DING, Z., ELKASHLAN, M., et al. Cooperative nonorthogonal access with simultaneous wireless information and power transfer. IEEE Journal on Selected Areas in Communications. 2016, vol. 34, no. 4, p. 938–953. DOI: 10.1109/JSAC.2016.2549378
10. VAN, H. T., NGUYEN, H. S., NGUYEN, T. S., et al. Outage performance analysis of non-orthogonal multiple access with timeswitching energy harvesting, Elekronika ir Elekronika, 2019, vol. 25, no. 3, p. 85–91. DOI: 10.5755/j01.eie.25.3.23682
11. XU, K., SHEN, Z., WANG, Y., et al. Hybrid time-switching and power splitting SWIPT for full-duplex massive MIMO systems: A beam-domain approach. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 8, p. 7257–7274. DOI: 10.1109/TVT.2018.2831790
12. LI, Z. Multiobjective optimization designs in downlink NOMA transmission systems. Wireless Communications and Mobile Computing, 2022, p. 1–9. DOI: 10.1155/2022/7268083
13. GUO, Y. X, LI, H. A power allocation method based on particle swarm algorithm for NOMA downlink networks. Journal of Physics: Conference Series (The First International Conference on Advanced Algorithms and Control Engineering), 2018, vol. 1087, p. 1–7. DOI: 10.1088/1742-6596/1087/2/022033
14. 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
15. MIRJALILI, S., SAREMI, S. MIRJALILI, S. M., et al. Multiobjective grey wolf optimizer: A novel algorithm for multicriterion optimization. Expert Systems with Applications, 2016, vol. 47, p. 106–119. DOI: 10.1016/j.eswa.2015.10.039
16. SHAHAB, B. M., IFRAN, 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
17. GRADSHTEYN, I. S., RYZHIK, I. M. Table of Integrals, Series and Products. 8th ed. Elsevier, 2014. ISBN: 978-0-12-384933-5
18. DEB, K., PRATAP, A., AGARWAL, S., et al. Fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, vol. 6, no. 2, p. 182–197. DOI: 10.1109/4235.996017
19. COELLO, C. A., PULIDO, G. T., LECHUGA, M. S. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, vol. 8, no. 3, p. 256–279. DOI: 10.1109/TEVC.2004.826067
20. SHI, X, KONG, D. A multi-objective ant colony optimization algorithm based on elitist selection strategy. Metallurgical & Mining Industry, 2015, vol. 7, no. 6, p. 333–338.
21. HANCER, E., XUE, B., ZHANG, M., et al. A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information. In Proceedings of IEEE Congress on the Evolutionary Computation (CEC). Sendai (Japan), 2015, p. 2420 to 2427. DOI: 10.1109/CEC.2015.7257185
22. YANG, X.-S. Bat algorithm for multi-objective optimization. International Journal of Bio-Inspired Computation, 2011, vol. 3, no. 4, p. 267–274. DOI: 10.1504/IJBIC.2011.042259
23. MAKHADMEH, S. N., ALOMARI, O. A., MIRJALILI, S., et al. Recent advances in multi-objective grey wolf optimizer, its versions and applications. Neural Computing and Applications, 2022, vol. 34, no. 6, p. 19723–19749. DOI: 10.1007/s00521-022-07704-5
24. MIRJALILI, S., MIRJALILI, S., LEWIS, A. Grey wolf optimizer. Advances in Engineering Software, 2014, vol. 69, p. 46–61. DOI: 10.1016/j.advengsoft.2013.12.007
25. MIRJALILI, S., DONG, J. S. Multi-Objective Optimization using Artificial Intelligence Techniques. 1st ed. Springer, 2019. DOI: 10.1007/978-3-030-24835-2
26. DO, D. T. Optimal energy harvesting scheme for power beaconassisted wireless-powered networks. Indonesian Journal of Electrical Engineering and Computer Science, 2017, vol. 7, no. 3, p. 802–808. DOI: 10.11591/ijeecs.v7.i3.pp802-808
27. TIN, P. T, DINH, B. H, NGUYEN, T. N., et al. Power beaconassisted energy harvesting wireless physical layer cooperative relaying networks: Performance analysis. Symmetry, 2020, vol. 12, no. 1, p. 1–13. DOI: 10.3390/sym12010106
28. LUO, Y., WU, C., LENG, Y., et al. Throughput optimization for NOMA cognitive relay network with RF energy harvesting based on improved bat algorithm. MDPI Mathematics, 2022, vol. 10, no. 22, p. 1–22. DOI: 10.3390/math10224357

Keywords: Base station, outage probability, power beacon, throughput, wireless power transfer, multi-objective optimization, Grey Wolf Optimizer (GWO), Multi-Objective Grey Wolf Optimizer (MOGWO), Pareto optimal solutions

W. Jlassi, R. Haddad, R.Bouallegue [references] [full-text] [DOI: 10.13164/re.2023.0502] [Download Citations]
Energy-Efficient Path Construction for Data Gathering Using Mobile Data Collectors in Wireless Sensor Networks

Energy is seen as a significant factor in wireless sensor networks (WSNs). It is a challenge to balance between battery lifetime of the different sensors and network lifetime. The main contribution of the proposed approach is to decrease the energy consumption of each sensor node, overcome unbalanced energy usage among sensor nodes, reduce the data gathering time and enhance the network lifetime. To achieve these goals, we combine the Hierarchical Agglomerative algorithm and an optimal path selection method. First, the suitable cluster heads (CHs) are elected based on the Euclidean distance and the residual energy of each sensor node. Then, the base station is situated at the center of the field, which will be partitioned into equal subareas, one for every mobile data collector (MDC). Second, the Kruskal algorithm is used to create an optimal data gathering path from each subset of elected cluster heads. Finally, each mobile data collector travels the optimal path to collect the data from the set of cluster heads of each subarea and returns periodically to the base station to upload gathered data. Computer simulation proves that the proposed approach outperforms existing ones in terms of data gathering time, residual energy and network lifetime

1. ROCHA, A., PIRMEZ, L., DELICATO, F., et al. WSNs clustering based on semantic neighborhood relationships. Computer Networks Journal, 2012, vol. 56, no. 5, p. 1627–1645. DOI: 10.1016/j.comnet.2012.01.014
2. KULAKOWSKI, P., CALLE, E., MARZO, J. L. Performance study of wireless sensor and actuator networks in forest fire scenarios. International Journal of Communication Systems, 2013, vol. 26, no. 4, p. 515–529. DOI: 10.1002/dac.2311
3. YICK, J., MUKHERJEE, B., GHOSAL, D. Wireless sensor network survey. Computer Networks, 2008, vol. 52, no. 12, p. 2292 to 2330. DOI: 10.1016/j.comnet.2008.04.002
4. MANJUNATHA, T. N., SUSHMA, M. D., SHIVAKUMAR, K. M. Security concepts and Sybil attack detection in wireless sensor networks. International Journal of Emerging Trends and Technology in Computer Science, 2013, vol. 2, no. 2, p. 383–390. ISSN: 2278-6856
5. MAHESHWARI, P., SHARMA, A., VERMA, K. Energy efficient cluster-based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 2021, vol. 110, p. 1–15. DOI: 10.1016/j.adhoc.2020.102317
6. EL FISSAOUI, M., BENI-HSSANE, A., SAADI, M. Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2019, vol. 10, no. 2, p. 569–578. DOI: 10.1007/s12652-018-0704-8
7. ABDOLKARIMI, M., ADABI, S., SHARIFI, A. A new multiobjective distributed fuzzy clustering algorithm for wireless sensor networks with mobile gateways. AEU-International Journal of Electronics and Communications, 2018, vol. 89, p. 92–104. DOI: 10.1016/j.aeue.2018.03.020
8. SHAH, R. C., ROY, S., JAIN, S., et al. Data MULEs: Modeling a three-tier architecture for sparse sensor networks. In Proceedings of IEEE Workshop on Sensor Network Protocols and Applications (SNPA). Anchorage (AK, USA), 2003, p. 30–41. DOI: 10.1109/SNPA.2003.1203354
9. CHEN, T., CHEN, T., WU, P. On data collection using mobile robot in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2011, vol. 41, no. 6, p. 1213–1224. DOI: 10.1109/TSMCA.2011.2157132
10. ZHAO, M., MA, M., YANG, Y. Mobile data gathering with spacedivision multiple access in wireless sensor network. In Proceedings of 27th Conference on Computer Communications (INFOCOM). Phoenix (AZ, USA), 2008, p. 1957–1965. DOI: 10.1109/INFOCOM.2008.185
11. SALARIAN, H., CHIN, K.-W., NAGHDY, F. An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 2014, vol. 63, no. 5, p. 2407–2419. DOI: 10.1109/TVT.2013.2291811
12. DONG, M., OTA, K., YANG, L. T., et al. LSCD: A low-storage clone detection protocol for cyber-physical systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016, vol. 35, no. 5, p. 712–723. DOI: 10.1109/TCAD.2016.2539327
13. DUAN, X., ZHAO, C., HE, S., et al., Distributed algorithms to compute Walrasian equilibrium in mobile crowdsensing. IEEE Transactions on Industrial Electronics, 2017, vol. 64, no. 5, p. 4048–4057. DOI: 10.1109/TIE.2016.2645138
14. GHOSH, N., BANERJEE, I., SHERRATT, R. S. On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network. Wireless Networks, 2019, vol. 25, no. 4, p. 1829–1845. DOI: 10.1007/s11276-017-1635-6
15. SERT, S. A., BAGCI, H., YAZICI, A. MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 2015, vol. 30, p. 151–165. DOI: 10.1016/j.asoc.2014.11.063
16. SOMASUNDARA, A. A, RAMAMOORTHY, A., SRIVASTAVA, M. B. Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines. In 25th IEEE International Real-Time Systems Symposium. Lisbon (Portugal), 2004, p. 296–305. DOI: 10.1109/REAL.2004.31
17. ZHANG, C., FEI, S. A matching game-based data collection algorithm with mobile collectors. Sensors, 2020, vol. 20, no. 5, p. 1–16. DOI: 10.3390/s20051398
18. YALÇIN, S., ERDEM, E. A mobile sink path planning for wireless sensor networks based on priority-ordered dependent nonparametric trees. International Journal of Communication Systems, 2020, vol. 33, no. 12, p. 1–19. DOI: 10.1002/dac.4449
19. HA, I., DJURAEV, M., AHN, B. An optimal data gathering method for mobile sinks in WSNs. Wireless Personal Communication, 2017, vol. 97, p. 1401–1417. DOI: 10.1007/s11277-017-4579-3
20. KAMAREI, M., PATOOGHY, A., SHAHSAVARI, Z., et al. Lifetime expansion in WSNs using mobile data collector: A learning automata approach. Journal of King Saud University - Computer and Information Sciences, 2018, vol. 32, no. 1, p. 65–72. DOI: 10.1016/j.jksuci.2018.03.006
21. ALPARSLAN, D. N., SOHRABY, K. Two-dimensional modeling and analysis of generalized random mobility models for wireless ad hoc networks. IEEE/ACM Transactions on Networking, 2007, vol. 15, no. 3, p. 616–629. DOI: 10.1109/TNET.2007.893873
22. RAO, X., HUANG, H., TANG, J., et al. Residual energy-aware mobile data gathering in wireless sensor networks. Journal of Telecommunications Systems, 2016, vol. 62, p. 31–41. DOI: 10.1007/s11235-015-9980-1
23. WU, Q., SUN, P., BOUKERCHE, A. A novel data collector path optimization method for lifetime prolonging in wireless sensor networks. In 2019 IEEE Global Communications Conference (GLOBECOM). Waikoloa (HI, USA), 2019, p. 1–6. DOI: 10.1109/GLOBECOM38437.2019.9013989
24. TASHTARIAN, F., YAGHMAEE MOGHADDAM, M. H., SOHRABY, K., et al. On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks. IEEE Transactions on Vehicular Technology, 2015, vol. 64, no. 7, p. 3177–3189. DOI: 10.1109/TVT.2014.2354338
25. ABIDOYE, A. P., KABASO, B. Energy-efficient hierarchical routing in wireless sensor networks based on fog computing. EURASIP Journal on Wireless Communications and Networking, 2021, p. 1–26. DOI: 10.1186/s13638-020-01835-w
26. SUN, Y., DONG, W., CHEN, Y. An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Communications Letters, 2019, vol. 21, no. 6, p. 1317–1320. DOI: 10.1109/LCOMM.2017.2672959
27. WEN, W., ZHAO, S., SHANG, C., et al. EAPC: Energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sensors Journal, 2020, vol. 18, no. 2, p. 890–901. DOI: 10.1109/JSEN.2017.2773119
28. JLASSI, W., HADDAD, R., BOUALLEGUE, R., et al. A combination of K-means algorithm and optimal path selection method for lifetime extension in wireless sensor networks. In International Conference on Advanced Information Networking and Applications. 2021, p. 416–425. DOI: 10.1007/978-3-030-75078-7_42
29. LAXMA REDDY, D., PUTTAMADAPPA, C., SURESHB, H. N. Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in wireless sensor network. Pervasive and Mobile Computing, 2021, vol. 71, p. 1–18. DOI: 10.1016/j.pmcj.2021.101338
30. JLASSI, W., HADDAD, R., BOUALLEGUE, R., SHUBAIR, R. Increase the lifetime of wireless sensor network using clustering algorithm and optimal path selection method. Radioengineering, 2022, vol. 31, no. 3, p. 301–311. DOI: 10.13164/re.2022.0301
31. EL FISSAOUI, M., BENI-HSSANE, A., SAADI, M. Energy aware hybrid scheme of client-server and mobile agent models for data aggregation in wireless sensor networks. In Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). 2016, p. 227–232. DOI: 10.1007/978-3-319-52941-7_23

Keywords: Wireless sensor network (WSN), optimal path, clustering, Mobile Data Collector (MDC), Cluster Head (CH)

R. Lu, H. Natiq, A. M. A. Ali, H. R. Abdolmohammadi, S. Jafari [references] [full-text] [DOI: 10.13164/re.2023.0511] [Download Citations]
Synchronization of Dissipative Nose–Hoover Systems: Circuit Implementation

The synchronization of dynamical systems has been extensively studied across various scientific disciplines, including secure communication, providing insights into the collective behavior of complex systems. This paper investigated the synchronization of diffusively coupled dissipative Nose-Hoover (DNH) systems analytically and experimentally. This system exhibits a variety of fascinating dynamical phenomena, including multistable or monostable chaotic solutions and attractive torus. The DNH circuit is implemented in OrCAD-PSpice, focusing on chaotic dynamics. The DNH system is thus said to be diffusively coupled by considering a passive resistor to link the corresponding states of two DNH circuits. The coupling scheme and strength (resistor value) under which two circuits can be synchronized are attained using the master stability function method and are then confirmed by computing the synchronization error. The correlation of coupled circuits' outputs (time evolutions) demonstrates complete synchronization, which is consistent with the analytical and experimental results

1. PETRZELA, J. Canonical hyperchaotic oscillators with single generalized transistor and generative two-terminal elements. IEEE Access, 2022, vol. 10, p. 90456–90466. DOI: 10.1109/ACCESS.2022.3201870
2. PETRZELA, J. Hyperchaotic self-oscillations of two-stage class C amplifier with generalized transistors. IEEE Access, 2021, vol. 9, p. 62182–62194. DOI: 10.1109/ACCESS.2021.3074367
3. PETRZELA, J., KOLKA, Z., HANUS, S. Simple chaotic oscillator: From mathematical model to practical experiment. Radioengineering, 2006, vol. 15, no. 1, p. 6–11. ISSN: 1210-2512
4. SPROTT, J. C., THIO, W. J.-C. Elegant Circuits: Simple Chaotic Oscillators. World Scientific, 2021. ISBN: 9789811240010
5. PETRZELA, J., HRUBOS, Z., GOTTHANS, T. Modeling deterministic chaos using electronic circuits. Radioengineering, 2011, vol. 20, no. 2, p. 438–444. ISSN: 1210-2512
6. ORAVEC, J., TURAN, J., OVSENIK, L., et al. Asymmetric image encryption approach with plaintext-related diffusion. Radioengineering, 2018, vol. 27, no. 1, p. 281–288. DOI: 10.13164/re.2018.0281
7. BERNSTEIN, G. M., LIEBERMAN, M. A. Secure random number generation using chaotic circuits. IEEE Transactions on Circuits and Systems, 1990, vol. 37, no. 9, p. 1157–1164. DOI: 10.1109/31.57604
8. LIN, H.,WANG, C., YU, F., et al. A review of chaotic systems based on memristive Hopfield neural networks. Mathematics, 2023, vol. 11, no. 6, p. 1–18. DOI: 10.3390/math11061369
9. MA, X., WANG, C. Hyper-chaotic image encryption system based on N + 2 ring Joseph algorithm and reversible cellular automata. Multimedia Tools and Applications, 2023, p. 1–26. DOI: 10.1007/s11042-023-15119-0
10. ZHU, Y., WANG, C., SUN, J., et al. A chaotic image encryption method based on the artificial fish swarms algorithm and the DNA coding. Mathematics, 2023, vol. 11, no. 3, p. 1–18. DOI: 10.3390/math11030767
11. MA, X., WANG, C., QIU, W., et al. A fast hyperchaotic image encryption scheme. International Journal of Bifurcation and Chaos, 2023, vol. 33, no. 5, p. 1–21. DOI: 10.1142/S021812742350061X
12. KUZNETSOV, N., MOKAEV, T., PONOMARENKO, V., et al. Hidden attractors in Chua circuit: Mathematical theory meets physical experiments. Nonlinear Dynamics, 2023, vol. 111, no. 6, p. 5859–5887. DOI: 10.1007/s11071-022-08078-y
13. WANG, J., XIAO, L., RAJAGOPAL, K., et al. Fractional-order analysis of modified Chua’s circuit system with the smooth degree of 3 and its microcontroller-based implementation with analog circuit design. Symmetry, 2021, vol. 13, no. 2, p. 5859–5887. DOI: 10.3390/sym13020340
14. BAO, B., LI, Q.,WANG,N., et al. Multistability in Chua’s circuit with two stable node-foci. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2016, vol. 26, no. 4, p. 1–9. DOI: 10.1063/1.4946813
15. CHEN, M., LI, M., YU, Q., et al. Dynamics of self-excited attractors and hidden attractors in generalized memristor-based Chua’s circuit. Nonlinear Dynamics, 2015, vol. 81, no. 1, p. 215–226. DOI: 10.1007/s11071-015-1983-7
16. XU, Q., LIN, Y., BAO, B., et al. Multiple attractors in a non-ideal active voltage-controlled memristor based Chua’s circuit. Chaos, Solitons & Fractals, 2016, vol. 83, p. 186–200. DOI: 10.1016/j.chaos.2015.12.007
17. GUO, M., YANG, W., XUE, Y., et al. Multistability in a physical memristor-based modified Chua’s circuit. Chaos, 2019, vol. 29, no. 4, p. 1–13. DOI: 10.1063/1.5089293
18. YE, X., WANG, X., GAO, S., et al. A new chaotic circuit with multiple memristors and its application in image encryption. Nonlinear Dynamics, 2020, vol. 99, no. 2, p. 1489–1506. DOI: 10.1007/s11071-019-05370-2
19. WANG, X., PHAM, V. T., JAFARI, S., et al. A new chaotic system with stable equilibrium: From theoretical model to circuit implementation. IEEE Access, 2017, vol. 5, p. 8851–8858. DOI: 10.1109/ACCESS.2017.2693301
20. AHMADI, A., RAJAGOPAL, K., ALSAADI, F. E., et al. A novel 5D chaotic system with extreme multi-stability and a line of equilibrium and its engineering applications: Circuit design and FPGA implementation. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2020, vol. 44, no. 1, p. 59–67. DOI: 10.1007/s40998-019-00223-5
21. XU, Q., WANG, Y., CHEN, B., et al. Firing pattern in a memristive Hodgkin–Huxley circuit: Numerical simulation and analog circuit validation. Chaos, Solitons & Fractals, 2023, vol. 172, p. 1–10. DOI: 10.1016/j.chaos.2023.113627
22. XU, Q., WANG, Y., IU, H. H. C., et al. Locally active memristorbased neuromorphic circuit: Firing pattern and hardware experiment. IEEE Transactions on Circuits and Systems I: Regular Papers, 2023, vol. 70, no. 8, p. 1–2. DOI: 10.1109/TCSI.2023.3276983
23. BOCCALETTI, S., KURTHS, J., OSIPOV, G., et al. The synchronization of chaotic systems. Physics Reports, 2002, vol. 366, no. 1–2, p. 1–101. DOI: 10.1016/S0370-1573(02)00137-0
24. ZHENG, Z., HU, G. Generalized synchronization versus phase synchronization. Physical Review E, 2000, vol. 62, no. 6, p. 7882–7885. DOI: 10.1103/PhysRevE.62.7882
25. BELYKH, V. N., OSIPOV, G. V., PETROV, V. S., et al. Cluster synchronization in oscillatory networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008, vol. 18, no. 3, p. 1–6. DOI: 10.1063/1.2956986
26. ROSENBLUM, M. G., PIKOVSKY, A. S., KURTHS, J. Phase synchronization of chaotic oscillators. Physical Review Letters, 1996, vol. 76, no. 11, p. 1804–1807. DOI: 10.1103/PhysRevLett.76.1804
27. ROSENBLUM, M. G., PIKOVSKY, A. S., KURTHS, J. From phase to lag synchronization in coupled chaotic oscillators. Physical Review Letters, 1997, vol. 78, no. 22, p. 4193–4196. DOI: 10.1103/PhysRevLett.78.4193
28. PARASTESH, F., JAFARI, S., AZARNOUSH, H., et al. Chimeras. Physics Reports, 2021, vol. 898, p. 1–114. DOI: 10.1016/j.physrep.2020.10.003
29. BOCCALETTI, S., PISARCHIK, A. N., GENIO, C. I., et al. Synchronization: From Coupled Systems to Complex Networks. Cambridge University Press, 2018. ISBN: 9781107056268
30. HUSSAIN, I., JAFARI, S., GHOSH, D., et al. Synchronization and chimeras in a network of photosensitive FitzHugh–Nagumo neurons. Nonlinear Dynamics, 2021, vol. 104, no. 3, p. 2711–2721. DOI: 10.1007/s11071-021-06427-x
31. GHOSH, D., BANERJEE, S., CHOWDHURY, A. R. Synchronization between variable time-delayed systems and cryptography.Europhysics Letters, 2007, vol. 80, no. 3, p. 1–6. DOI: 10.1209/0295-5075/80/30006
32. BANERJEE, S. Chaos Synchronization and Cryptography for Secure Communications: Applications for Encryption. Information Science Reference, 2010. ISBN: 9781615207381
33. ZHOU, L., TAN, F., LI, X., et al. A fixed-time synchronizationbased secure communication scheme for two-layer hybrid coupled networks. Neurocomputing, 2021, vol. 433, p. 131–141. DOI: 10.1016/j.neucom.2020.12.033
34. FAN, W., WU, H., LI, Z., et al. Synchronization and chimera in a multiplex network of Hindmarsh–Rose neuron map with flux-controlled memristor. The European Physical Journal Special Topics, 2022, vol. 231, no. 22, p. 4131–4141. DOI: 10.1140/epjs/s11734-022-00720-5
35. RAKSHIT, S., RAY, A., BERA, B. K., et al. Synchronization and firing patterns of coupled Rulkov neuronal map. Nonlinear Dynamics, 2018, vol. 94, no. 2, p. 785–805. DOI: 10.1007/s11071-018-4394-8
36. PARASTESH, F., MEHRABBEIK, M., RAJAGOPAL, K., et al. Synchronization in Hindmarsh–Rose neurons subject to higher-order interactions. Chaos: An Interdisciplinary Journal ofNonlinear Science, 2022, vol. 32, no. 1, p. 1–11. DOI: 10.1063/5.0079834
37. XU, Q., LIU, T., DING, S., et al. Extreme multistability and phase synchronization in a heterogeneous bi-neuronRulkov network with memristive electromagnetic induction. Cognitive Neurodynamics, 2023, vol. 17, no. 3, p. 755–766. DOI: 10.1007/s11571-022-09866-3
38. PECORA, L. M., CARROLL, T. L. Master stability functions for synchronized coupled systems. Physical Review Letters, 1998, vol. 80, no. 10, p. 2109–2112. DOI: 10.1103/PhysRevLett.80.2109
39. BUSCARINO, A., FORTUNA, L., FRASCA, M., et al. Chua’s circuits synchronization with diffusive coupling: New results. International Journal of Bifurcation and Chaos, 2009, vol. 19, no. 9, p. 3103–3107. DOI: 10.1142/S0218127409024670
40. LU, R., RAMAKRISHNAN, B., FALAH, M. W., et al. Synchronization and different patterns in a network of diffusively coupled elegant Wang–Zhang–Bao circuits. The European Physical Journal Special Topics, 2022, vol. 231, no. 22, p. 3987–3997. DOI: 10.1140/epjs/s11734-022-00690-8
41. MISHKOVSKI, I., MIRCHEV, M., CORINTO, F., et al. Synchronization analysis of networks of identical and nearly identical Chua’s oscillators. In IEEE International Symposium on Circuits and Systems (ISCAS). Seoul (South Korea), 2012, p. 2115–2118. DOI: 10.1109/ISCAS.2012.6271703
42. CAO, B., WANG, Y.-F., WANG, L., et al. Cluster synchronization in complex network of coupled chaotic circuits: An experimental study. Frontiers of Physics, 2018, vol. 13, no. 5, p. 1–11. DOI: 10.1007/s11467-018-0775-1
43. GAMBUZZA, L. V., FRASCA, M., GOMEZ-GARDEÑES, J. Intralayer synchronization in multiplex networks. Europhysics Letters, 2015, vol. 110, no. 2, p. 1–6. DOI: 10.1209/0295-5075/110/20010
44. BURBANO-L, D. A., YAGHOUTI, S., PETRARCA, C., et al. Synchronization in multiplex networks of Chua’s circuits: Theory and experiments. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, vol. 67, no. 3, p. 927–938. DOI: 10.1109/TCSI.2019.2955972
45. JAFARI, S., SPROTT, J. C., DEHGHAN, S. Categories of conservative flows. International Journal of Bifurcation and Chaos, 2019, vol. 27, no. 2, p. 1–16. DOI: 10.1142/S0218127419500214
46. MEHRABBEIK, M., JAFARI, S., SPROTT, J. C. A simple threedimensional quadratic flowwith an attracting torus. Physics Letters A, 2022, vol. 451, p. 1–8. DOI: 10.1016/j.physleta.2022.128427
47. WOLF, A., SWIFT, J. B., SWINNEY, H. L., et al. Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 1985, vol. 16, no. 3, p. 285–317. DOI: 10.1016/0167-2789(85)90011-9
48. HUANG, L., CHEN, Q., LAI, Y.-C., et al. Generic behavior of master-stability functions in coupled nonlinear dynamical systems. Physical Review E, 2009, vol. 80, no. 3, p. 1–11. DOI: 10.1103/PhysRevE.80.036204

Keywords: Synchronization, dissipative Nose–Hoover system, master stability function, chaotic circuit

M. Rujzl, M. Sigmund [references] [full-text] [DOI: 10.13164/re.2023.0523] [Download Citations]
Depersonalization of Speech Using Speaker-Specific Transform Based on Long-Term Spectrum

This paper introduces a novel approach for hiding personal information in speech signals. The proposed approach applied a transform warping function, which is obtained from a long-term linear prediction spectrum individually for each speaker. The depersonalized speech was compared with the often used technique based on vocal tract length normalization. The proposed approach performs wider manipulation of fundamental frequency and provides higher intelligibility by 5% in clean speech and by 8% for signal-to-noise ratio 5 dB. It also significantly alters the derived glottal pulses, making them difficult to use for personality analysis. Speech intelligibility index and glottal pulse distortion are new aspects in the field of voice depersonalization.

1. KRZYSZTOFEK, M. GDPR: General Data Protection Regulation (EU) 2016/679: Post-reform Personal Data Protection in the European Union. Alphen aan den Rijn (The Netherlands) : Wolters Kluwer, 2019. ISBN: 9789403505947
2. YOO, I. C., LEE, K., LEEM, S., et al. Speaker anonymization for personal information protection using voice conversion techniques. IEEE Access, 2020, vol. 8, p. 198637–198645. DOI: 10.1109/ACCESS.2020.3035416
3. MAGARINOS, C., LOPEZ-OTERO, P., DOCIO-FERNANDEZ, L., et al. Reversible speaker de-identification using pre-trained transformation functions. Computer Speech and Language, 2017, vol. 46, p. 36–52. DOI: 10.1016/j.csl.2017.05.001
4. JUSTIN, T., STRUC, V., DOBRISEK, S., et al. Speaker deidentification using diphone recognition and speech synthesis. In Proceedings of the International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Ljubljana (Slovenia), 2015, p. 1–7. DOI: 10.1109/FG.2015.7285021
5. ZEN, H., NOSE, T., YAMAGISHI, J., et al. The HMM-based speech synthesis system (HTS) version 2.0. In ISCA Tutorial and Research Workshop on Speech Synthesis (SSW). Bonn (Germany), 2007, p. 294–299.
6. TOMA, S. A., TARSA, G. I., OANCEA, E., et al. A TD-PSOLA based method for speech synthesis and compression. In 8th International Conference on Communications (COMM). Bucharest (Romania), 2010, p. 123–126. DOI: 10.1109/ICCOMM.2010.5509044
7. PERROT, P., AVERSANO, G., CHOLLET, G. Voice disguise and automatic detection: Review and perspectives. Chapter in: STYLIANOU, Y., FAUNDEZ-ZANUY, M., ESPOSITO, A. (eds). Progress in Nonlinear Speech Processing. Berlin, Heidelberg: Springer, 2007, p. 101–117. DOI: 10.1007/978-3-540-71505-4_7
8. GARCIA-MATEO, C., CHOLLET, G. (eds). Voice Biometrics - Technology, Trust and Security. London (UK): The Institution of Engineering and Technology, 2021. ISBN: 9781785619007
9. SATHIAREKHA, K., KUMARESAN, S. A survey on the evolution of various voice conversion techniques. In 3rd International Conference on Advanced Computing and Communication Systems (ICACCS). Coimbatore (India), 2016, p. 1–5. DOI: 10.1109/ICACCS.2016.7586373
10. ABE, M., NAKAMURA, S., SHIKANO, K., et al. Voice conversion through vector quantization. Journal of the Acoustical Society of Japan (E), 1990, vol. 11, no. 2, p. 71–76. DOI: 10.1250/ast.11.71
11. TODA, A., BLACK, W., TOKUDA, K. Voice conversion based on maximum likelihood estimation of spectral parameter trajectory. IEEE Transactions on Audio, Speech, and Language Processing, 2007, vol. 15, no. 8, p. 2222–2235. DOI: 10.1109/TASL.2007.907344
12. DESAI S., RAGHAVENDRA, E. V., YEGNANARAYANA, B., et al. Voice conversion using artificial neural networks. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Taipei (Taiwan), 2009, p. 3893–3896. DOI: 10.1109/ICASSP.2009.4960478
13. SUNDERMANN, D., NEY, H. VTLN-based voice conversion. In IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Darmstadt (Germany), 2003, p. 556–559. DOI: 10.1109/ISSPIT.2003.1341181
14. ERRO, D., MORENO, A., BONAFONTE, A. Voice conversion based on weighted frequency warping. IEEE Transactions on Audio, Speech, and Language Processing, 2010, vol. 18, no. 5, p. 922–931. DOI: 10.1109/TASL.2009.2038663
15. SERIZEL, R., GIULIANI, D. Vocal tract length normalisation approaches to DNN-based children’s and adults’ speech recognition. In IEEE Spoken Language Technology Workshop (SLT). South Lake Tahoe (NV, USA), 2014, p. 135–140. DOI: 10.1109/SLT.2014.7078563
16. ERRO, D., NAVAS, E., HERNAEZ, I. Parametric voice conversion based on bilinear frequency warping plus amplitude scaling. IEEE Transactions on Audio, Speech, and Language Processing, 2013, vol. 21, no. 3, p. 556–566. DOI: 10.1109/TASL.2012.2227735
17. RABINER, L. R., SCHAFER, R. W. Theory and Applications of Digital Speech Processing. London (UK): Prentice Hall, 2011. ISBN: 9780136034285
18. SIGMUND, M. Speaker discrimination using long-term spectrum of speech. Information Technology and Control, 2019, vol. 48, no. 3, p. 446–453. DOI: 10.5755/j01.itc.48.3.21248
19. SIGMUND, M. Spectral analysis of speech under stress. International Journal of Computer Science and Network Security, 2007, vol. 7, no. 4, p. 170–172. ISSN: 1738-7906
20. RUJZL, M., SIGMUND, M. Speech depersonalization based on the long-term spectrum of voice. Authorea, 2022, (preprint). DOI: 10.22541/au.166436464.40383121/v1
21. TAGHAVI, S. M., MOHAMMADKHANI, G., JALILVAND, H. Speech intelligibility index: A literature review. Auditory and Vestibular Research, 2022, vol. 31, no. 3, p. 148–157. DOI: 10.18502/avr.v31i3.9861
22. ANSI. Methods for Calculation of the Speech Intelligibility Index. ANSI S3.5-1997 [R2007]. New York, 2007.
23. DRUGMAN, T., ALWAN, A. Joint robust voicing detection and pitch estimation based on residual harmonics. In Proceedings of the 12th Annual Conference of the International Speech Communication Association (INTERSPEECH). Florence (Italy), 2011, p. 1973–1976. DOI: 10.21437/Interspeech.2011-519
24. NOURTEL, H., CHAMPION, P., JOUVET, D., et al. Evaluation of speaker anonymization on emotional speech. In ISCA Symposium on Security and Privacy in Speech Communication (SPSC). 2021, p. 62–66. DOI: 10.21437/SPSC.2021-13
25. 530 M. RUJZL, M. SIGMUND, DEPERSONALIZATION OF SPEECH USING SPEAKER-SPECIFIC TRANSFORM BASED ON . . .
26. SIGMUND, M., DOSTAL, T. Analysis of emotional stress in speech. In Proceedings of International Conference on Artificial Intelligence and Applications. Innsbruck (Austria), 2004, p. 317–322.
27. AALTO UNIVERSITY. AALTO APARAT. [Online] Cited 2023-06-15. Available at: research.spa.aalto.fi/projects/aparat/
28. STANEK, M., SIGMUND, M. Psychological stress detection in speech using return-to-opening phase ratios in glottis. Elektronika ir Elektrotechnika, 2015, vol. 21, no. 5, p. 59–63. DOI: 10.5755/j01.eee.21.5.13336
29. PRIBIL, J., PRIBILOVA, A., MATOUSEK, J. Evaluation of speaker de-identification based on voice gender and age conversion. Journal of Electrical Engineering, 2018, vol. 69, no. 2, p. 138–147. DOI: 10.2478/jee-2018-0017

Keywords: Speech depersonalization, long-term spectrum, voice transformation, depersonalized speech evaluation

K. Tamizhelakkiya, S. Gauni, P. Chandhar [references] [full-text] [DOI: 10.13164/re.2023.0531] [Download Citations]
Transfer Learning based Location-Aided Modulation Classification in Indoor Environments for Cognitive Radio Applications

Modulation classification is a crucial technique to utilize the unconsumed spectrum in Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) systems to meet the required traffic demands for future-generation cellular networks. This paper presents an end-to-end experimental setup as a generic methodology to implement various Transfer Learning (TL) models in an indoor environment. This allows us to learn the features from multiple modulation signals to train and test the model. The performance evaluation of proposed TL models such as Convolutional Neural Network-Random Forest (CNN-RF), and Convolutional Long Short Term Deep Neural Network (CLDNN) -Random Forest (CLDNN-RF) have been thoroughly discussed. The result shows that the proposed TL models yield more than 90% classification accuracy for various modulation types. A proposed framework for location-specific TL model selection based on the maximum classification accuracy has been investigated.

1. AKYILDIZ, I. F., KAK, A., NIE, S. 6G and beyond: The future of wireless communications systems. IEEE Access, 2020, vol. 8, p. 133995–134030. DOI: 10.1109/ACCESS.2020.3010896
2. LETAIEF, K. B., CHEN, W., SHI, Y., et al. The roadmap to 6G: AI-empowered wireless networks. IEEE Communications Magazine, 2019, vol. 57, no. 8, p. 84–90. DOI: 10.1109/MCOM.2019.1900271
3. ALSHARIF, M. H., KELECHI, A. H., ALBREEM, M. A., et al. Sixth generation (6G) wireless networks: Vision, research activities, challenges, and potential solutions. Symmetry, 2020, vol. 12, no. 4. DOI: 10.3390/sym12040676
4. AKHTAR, T., TSELIOS, C., POLITIS, I. Radio resource management: Approaches and implementations from 4G to 5G and beyond. Wireless Networks, 2021, vol. 27, no. 1, p. 693–734. DOI: 10.1007/s11276-020-02479-w
5. CHETTRI, L., BERA, R. A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet of Things Journal, 2020, vol. 7, no. 1, p. 16–32. DOI: 10.1109/JIOT.2019.2948888
6. JI, H., PARK, S., YEO, J., et al. Ultra-reliable and low-latency communications in 5G downlink: Physical layer aspects. IEEE Wireless Communications, 2018, vol. 25, no. 3, p. 124–130. DOI: 10.1109/MWC.2018.1700294
7. CHALLITA, U., SANDBERG, D. Deep reinforcement learning for dynamic spectrum sharing of LTE and NR. In Proceedings of the IEEE International Conference on Communications. Montreal (QC, Canada), 2021, p. 1–6. DOI: 10.1109/ICC42927.2021.9500325
8. TSAKMALIS, A., CHATZINOTAS, S., TERSTEN, B. Automatic modulation classification for adaptive power control in cognitive satellite communications. In Proceedings of the 7th Advanced Satellite Multimedia Systems IEEE Conference and the 13th Signal Processing for Space CommunicationsWorkshop (ASMS/SPSC). Livorno (Italy), 2014, p. 234–240. DOI: 10.1109/ASMS-SPSC.2014.6934549
9. POLAK, L., KALLER, O., KLOZAR, L., et al. Influence of mobile network interfering products on DVB-T/H broadcasting services. In Proceedings of the IFIP Wireless Days. Dublin (Ireland), 2012, p. 1–5. DOI: 10.1109/WD.2012.6402860.
10. SHI, Y., DAVASLIOGLU, K., SAGDUYU, Y. E., et al. Deep learning for RF signal classification in unknown and dynamic spectrum environments. In Proceedings of the IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). Newark (NJ, USA), 2019, p. 1–10. DOI: 10.1109/DySPAN.2019.8935684
11. TENG, C.-F., CHOU, C.-Y., CHEN, C.-H., et al. Accumulated polar feature-based deep learning for efficient and lightweight automatic modulation classification with channel compensation mechanism. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 12, p. 15472–15485. DOI: 10.1109/TVT.2020.3041843
12. HATZICHRISTOS, G., FARGUES, M. P. A hierarchical approach to the classification of digital modulation types in multipath environments. In Proceedings of the Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. Pacific Grove (CA, USA), 2001, p. 1494–1498. DOI: 10.1109/ACSSC.2001.987737
13. KIM, K., AKBAR, I. A., BAE, K., et al. Cyclostationary approaches to signal detection and classification in cognitive radio. In Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). Dublin (Ireland), 2007, p. 212–215. DOI: 10.1109/DYSPAN.2007.35
14. TURCANIK, M., PERDOCH, J. SAMPLE dataset objects classification using deep learning algorithms. Radioengineering, 2023, vol. 32, no. 1, p. 63–73. DOI: 10.13164/re.2023.0063
15. EBRAHIMZADEH, A., GHAZALIAN, R. Blind digital modulation classification in software radio using the optimized classifier and feature subset selection. Engineering Applications of Artificial Intelligence, 2011, vol. 24, no. 1, p. 50–59. DOI: 10.1016/j.engappai.2010.08.008
16. CHEN, Y., SHAO, W., LIU, J., et al. Automatic modulation classification scheme based on LSTM with random erasing and attention mechanism. IEEE Access, 2020, vol. 8, p. 154290–154300. DOI: 10.1109/ACCESS.2020.3017641
17. JIA, F., YANG, Y., ZHANG, J., et al. A hybrid attention mechanism for blind automatic modulation classification. Transactions on Emerging Telecommunications Technologies, 2022, vol. 33, no. 7, p. 1–18. DOI: 10.1002/ett.4503
18. PIJACKOVA, K., GOTTHANS, T. Radio modulation classification using deep learning architectures. In Proceedings of the 31st IEEE International Conference Radioelektronika (RADIOELEKTRONIKA). Brno, (Czech Republic), 2021, p. 1–5. DOI: 10.1109/RADIOELEKTRONIKA52220.2021.9420195
19. ELSAGHEER, M. M., RAMZY, S. M. Hybrid model for automatic modulation classification based on residual neural networks and long short term memory. Alexandria Engineering Journal, 2023, vol. 67, p. 117–128. DOI: 10.1016/j.aej.2022.08.019
20. KULIN, M., KAZAZ, T., MOERMAN, I., et al. End-to-end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications. IEEE Access, 2017, vol. 6, p. 18484–18501. DOI: 10.1109/ACCESS.2018.2818794
21. SHI, J., HONG, S., CAI, C., et al. Deep learning-based automatic modulation recognition method in the presence of phase offset. IEEE Access, 2020, p. 1–10. DOI: 10.1109/ACCESS.2020.2978094
22. ALHAZMI, M. H., ALYMANI, M., ALHAZMI, H., et al. 5G signal identification using deep learning. In Proceedings of theWireless and Optical Communications Conference (WOCC). Newark (NJ, USA), 2020, p. 1–5. DOI: 10.1109/WOCC48579.2020.9114912
23. GRAVELLE, C., ZHOU, R. SDR demonstration of signal classification in real-time using deep learning. In Proceedings of the IEEE Globecom Workshops (GC Wkshps). Waikoloa (HI, USA), 2019, p. 1–5. DOI: 10.1109/GCWkshps45667.2019.9024661
24. JAGANNATH, J., SAARINEN, H. M., DROZDA, L. Framework for automatic signal classification techniques (FACT) for software-defined radios. In Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA). Verona (NY, USA), 2015, p. 1–7. DOI: 10.1109/CISDA.2015.7208628
25. BJORSELL, N., VITO, L., RAPUANO, S. A GNU radiobased signal detector for cognitive radio systems. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference. Hangzhou, (China), 2011, p. 1–5. DOI: 10.1109/IMTC.2011.5944235
26. CRAWFORD, M., KHOSHGOFTAAR, T. M. Using inductive transfer learning to improve hotel reviewspam detection. In Proceedings of the 22nd International Conference on Information Reuse and Integration for Data Science (IRI). Las Vegas (NV, USA), 2021, p. 248–254. DOI: 10.1109/IRI51335.2021.00040
27. MOREO, A., ESULI, A., SEBASTIANI, F. Lost in transduction: Transductive transfer learning in text classification. ACM Transactions on Knowledge Discovery from Data, 2021, vol. 16, no. 1, p. 1–21. DOI: 10.1145/3453146
28. COHN, R., HOLM, E. Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data. Integrating Materials and Manufacturing Innovation, 2021, vol. 10, no. 2, p. 231–244. DOI: 10.1007/s40192-021-00205-8
29. PAN, S. J., YANG, Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, vol. 22, no. 10, p. 1345–1359. DOI: 10.1109/TKDE.2009.191
30. ZHUANG, F., QI, Z., DUAN, K., et al. A comprehensive survey on transfer learning. Proceedings of the IEEE, 2021, vol. 109, no. 1, p. 43–76. DOI: 10.1109/JPROC.2020.3004555
31. BU, K., HE, Y., JING, X., et al. Adversarial transfer learning for deep learning based automatic modulation classification. IEEE Signal Processing Letters, 2020, vol. 27, p. 880–884. DOI: 10.1109/LSP.2020.2991875
32. MOYAZZOMA, R., HOSSAIN, M. A. A., ANUZ, M. H., et al. Transfer learning approach for plant leaf disease detection using CNN with pre-trained feature extraction method Mobilnetv2. In Proceedings of the 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). Dhaka, (Bangladesh), 2021, p. 526–529. DOI: 10.1109/ICREST51555.2021.9331214
33. MA, P., ZHANG, H., FAN, W., et al. A novel bearing fault diagnosis method based on 2D image representation and transfer learning convolutional neural network. Measurement Science and Technology, 2019, vol. 30, no. 5, p. 1–16. DOI: 10.1088/1361-6501/ab0793
34. BANSAL, M., KUMAR, M., SACHDEVA, M., et al. Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of Ambient Intelligence and Humanized Computing, 2021, vol. 14, p. 3609–3620. DOI: 10.1007/s12652-021-03488-z
35. TAMIZHELAKKIYA, CHANDHAR, P., GAUNI, S. Comparison of deep architectures for indoor RF signal classification. In Proceedings of the International Conference on Emerging Techniques in Computational Intelligence (ICETCI). Hyderabad (India), 2021, p. 107–112. DOI: 10.1109/ICETCI51973.2021.9574083
36. ZHOU, Q., ZHANG, R., ZHANG, F., et al. An automatic modulation classification network for IoT terminal spectrum monitoring under zero-sample situations. EURASIP Journal on Wireless Communications and Networking, 2022, vol. 2022, no. 1, p. 1–18. DOI: 10.1186/s13638-022-02099-2
37. O’SHEA, T. J., WEST, N. Radio machine learning dataset generation with GNU radio. Proceedings of the 6th GNU Radio Conference, 2016, vol. 1, no. 1, p. 1–6. [Online] Available at: https://pubs.gnuradio.org/index.php/grcon/article/view/11
38. LIN, Y., TU, Y., DOU, Z., et al. Contour stella image and deep learning for signal recognition in the physical layer. IEEE Transactions on Cognitive Communications and Networking, 2021, vol. 7, no. 1, p. 34–46. DOI: 10.1109/TCCN.2020.3024610

Keywords: Deep Learning (DL), modulation classification, CNN, Software Defined Radio (SDR), Transfer Learning (TL)

Y. V. Pershin [references] [full-text] [DOI: 10.13164/re.2023.0542] [Download Citations]
SPICE Modeling of Memcomputing Logic Gates

Memcomputing logic gates generalize the traditional Boolean logic gates for operation in the reverse direction. According to the literature, this functionality enables efficient solution of computationally intensive problems, including factorization and NP-complete problems. To approach the deployment of memcomputing gates in hardware, this paper introduces SPICE models of memcomputing logic gates following their original definition. Using these models, we demonstrate the behavior of single gates as well as small self-organizing circuits. We have also corrected some inconsistencies in the prior literature. Notably, the correct schematics of the dynamic correction module is reported here for the first time. Our work makes memcomputing more accessible to those interested in this emerging computing technology.

1. TRAVERSA, F. L., DI VENTRA, M. Polynomial-time solution of prime factorization and NP-complete problems with digital memcomputing machines. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, vol. 27, no. 2, p. 1–22. DOI: 10.1063/1.4975761
2. DI VENTRA, M. MemComputing: Fundamentals and Applications. 1st ed. Oxford (UK): Oxford University Press, 2022. ISBN: 9780192659897
3. DI VENTRA, M., TRAVERSA, F. L. Absence of chaos in digital memcomputing machines with solutions. Physics Letters A, 2017, vol. 381, no. 38, p. 3255–3257. DOI: 10.1016/j.physleta.2017.08.040
4. DI VENTRA, M., TRAVERSA, F. L. Absence of periodic orbits in digital memcomputing machines with solutions. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, vol. 27, no. 10, p. 1–1. DOI: 10.1063/1.5004431
5. DI VENTRA, M., TRAVERSA, F. L., OVCHINNIKOV, I. V. Topological field theory and computing with instantons. Annalen der Physik, 2017, vol. 529, no. 12, p. 1–6. DOI: 10.1002/andp.201700123
6. DI VENTRA, M., OVCHINNIKOV, I. V. Digital memcomputing: From logic to dynamics to topology. Annals of Physics, 2019, vol. 409, p. 1–12. DOI: 10.1016/j.aop.2019.167935
7. KOWALSKY, M., ALBASH, T., HEN, I., et al. 3-regular threeXORSAT planted solutions benchmark of classical and quantum heuristic optimizers. Quantum Science and Technology, 2022, vol. 7, no. 2, p. 1–18. DOI: 10.1088/2058-9565/ac4d1b
8. GOTO, H., TATSUMURA, K., DIXON, A. R. Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems. Science Advances, 2019, vol. 5, no. 4, p. 1–9. DOI: 10.1126/sciadv.aav2372
9. MATSUBARA, S., TAKATSU, M., MIYAZAWA, T., et al. Digital annealer for high-speed solving of combinatorial optimization problems and its applications. In 25th Asia and South Pacific Design Automation Conference (ASP-DAC). Beijing (China), 2020, p. 667–672. DOI: 10.1109/ASP-DAC47756.2020.9045100
10. BEARDEN, S. R. B., MANUKIAN, H., TRAVERSA, F. L., et al. Instantons in self-organizing logic gates. Physical Review Applied, 2018, vol. 9, no. 3, p. 1–8. DOI: 10.1103/PhysRevApplied.9.034029
11. CHUA, L. O., KANG, S. M. Memristive devices and systems. Proceedings of IEEE, 1976, vol. 64, no. 2, p. 209–223. DOI: 10.1109/PROC.1976.10092
12. DI VENTRA, M., PERSHIN, Y. V., CHUA, L. O. Circuit elements with memory: Memristors, memcapacitors, and meminductors. Proceedings of the IEEE, 2009, vol. 97, no. 10, p. 1717–1724. DOI: 10.1109/JPROC.2009.2021077
13. PERSHIN, Y. V., DI VENTRA, M. Memory effects in complex materials and nanoscale systems. Advances in Physics, 2011, vol. 60, no. 2, p. 145–227. DOI: 10.1080/00018732.2010.544961
14. OCHS, K., MICHAELIS, D., SOLAN, E. Towards wave digital memcomputing with physical memristor models. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, vol. 67, no. 4, p. 1092–1102. DOI: 10.1109/TCSI.2019.2953653
15. BEARDEN, S. R. B., PEI, Y. R., DI VENTRA, M. Efficient solution of Boolean satisfiability problems with digital memcomputing. Scientific Reports, 2020, vol. 10, no. 1, p. 1–8. DOI: 10.1038/s41598-020-76666-2
16. ERCSEY-RAVASZ, M., TOROCZKAI, Z. Optimization hardness as transient chaos in an analog approach to constraints satisfaction. Nature Physics, 2011, vol. 7, no. 12, p. 966–970. DOI: 10.1038/NPHYS2105
17. MOLNAR, F., KHAREL, S. R., HU, X. S., et al. Accelerating a continuous-time analog SAT solver using GPUs, Computer Physics Communications, 2020, vol. 256, p. 1–14. DOI: 10.1016/j.cpc.2020.107469
18. NGUYEN, D. C., ZHANG, Y.-H., DI VENTRA, M. et al. Hardware implementation of digital memcomputing on small-size FPGAs. In Proceedings of IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS). Phoenix (Arizona, USA), 2023, arXiv:2305.01061. [Forthcoming]
19. DI VENTRA, M., PERSHIN, Y. V. Memristors and Memelements: Mathematics, Physics, and Fiction. 1st ed. Cham (Switzerland): Springer, 2023. ISBN: 9783031256240
20. BIOLEK, Z., BIOLEK, D., BIOLKOVA, V. SPICE modeling of memristive, memcapacitative and meminductive systems. In Proceedings of European Conference on Circuit Theory and Design (ECCTD). Antalya (Turkey), 2009, p. 249–252. DOI: 10.1109/ECCTD.2009.5274934
21. PERSHIN, Y. V., DI VENTRA, M. SPICE model of memristive devices with threshold. Radioengineering, 2013, vol. 22, no. 2, p. 485–489. ISSN: 1210-2512
22. BIOLEK, D., DI VENTRA, M., PERSHIN, Y. V. Reliable SPICE simulations of memristors, memcapacitors and meminductors. Radioengineering, 2013, vol. 22, no. 4, p. 945–968. ISSN: 1210-2512
23. XU, K. D., ZHANG, Y. H., WANG, L., et al. Two memristor SPICE models and their applications in microwave devices. IEEE Transactions on Nanotechnology, 2014, vol. 13, no. 3, p. 607–616. DOI: 10.1109/TNANO.2014.2314126
24. VOURKAS, I., BATSOS, A., SIRAKOULIS, G. C. SPICE modeling of nonlinear memristive behavior. International Journal of Circuit Theory and Applications, 2015, vol. 43, no. 5, p. 553–565. DOI: 10.1002/cta.1957
25. LI, Q., SERB, A., PRODROMAKIS, T., et al. A memristor SPICE model accounting for synaptic activity dependence. PloS One, 2015, vol. 10, no. 3, p. 1–12. DOI: 10.1371/journal.pone.0120506
26. BIOLEK, D., KOLKA, Z., BIOLKOVA, V., et al. Memristor models for SPICE simulation of extremely large memristive networks. In Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS). Montreal (QC, Canada), 2016, p. 389–392. DOI: 10.1109/ISCAS.2016.7527252
27. GARCIA-REDONDO, F., GOWERS, R. P., CRESPO-YEPES, A., et al. SPICE compact modeling of bipolar/unipolar memristor switching governed by electrical thresholds. IEEE Transactions on Circuits and Systems I: Regular Papers, 2016, vol. 63, no. 8, p. 1255–1264. DOI: 10.1109/TCSI.2016.2564703
28. SCHROEDTER, R., DEMIRKOL, A. S., ASCOLI, A., et al. SPICE compact model for an analog switching niobium oxide memristor. In: Proceedings of 11th International Conference on Modern Circuits and Systems Technologies (MOCAST). Bremen (Germany), 2022, p. 1–4. DOI: 10.1109/MOCAST54814.2022.9837726
29. DOWLING, V. J., SLIPKO, V. A., PERSHIN, Y. V. Analytic and SPICE modeling of stochastic ReRAM circuits. In SPIE Proceedings of International Conference on Micro- and Nano-Electronics. Zvenigorod (Russia), 2021, p. 1–9. DOI: 10.1117/12.2624571
30. VLADIMIRESCU, A. The SPICE Book. 1st ed. New York (USA): Wiley, 1994. ISBN: 9780471609261
31. KUNDERT, K. The Designer’s Guide to SPICE and SPECTRE. New York (USA): Kluwer Academic Publishers, 1995. ISBN: 9780792395713
32. DI VENTRA, M., TRAVERSA, F. L. Self-Organizing Logic Gate and Circuits and Complex Problem Solving with SelfOrganizing Circuits. US Patent 9911080, 2018. Available at: https://patents.google.com/patent/US9911080
33. STRUKOV, D. B., SNIDER, G. S., STEWART, D. R., et al. The missing memristor found. Nature, 2008, vol. 453, no. 7191, p. 80–83. DOI: 10.1038/nature06932
34. ZHANG, Y.-H., DI VENTRA, M. Directed percolation and numerical stability of simulations of digital memcomputing machines. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2021, vol. 31, no. 6, p. 1–12. DOI: 10.1063/5.0045375

Keywords: Memristors, SPICE, nonlinear dynamical systems, computing technology

V. Kral [references] [full-text] [DOI: 10.13164/re.2023.0557] [Download Citations]
Design of Multi-bit Pulsed Latches with Scan Input in CMOS ONK65 Technology

This paper presents a new multi-bit pulse latch design that places innovative emphasis on the integration of scan input for automatic test pattern generation (ATPG). Two different designs have been developed in ONK65 technology (65 nm process): the first with standard threshold voltage (SVT) tailored for consumer products and the second with high threshold voltage (HVT) for automotive, each addressing specific aspects of process, voltage, and temperature (PVT). Multi-bit pulse latches offer a more efficient alternative to multi-bit flip-flop circuits and promise significant power and area savings. However, the efficiency of these latches depends on the technology, library type and customer requirements. A multi-bit pulse latch consists of a pulse generator and a pulsed latch. Each component is carefully designed for its specific purpose and the most appropriate topology is selected. Furthermore, the paper serves as a comprehensive guide to the design of low-power digital cells. It rethinks the topology design approach by emphasizing the scan input and presents simulation results for both components of the multi-bit pulse latch, highlighting their advantages. The results show that a less strict PVT offers greater benefits than a strict PVT.

1. WU, X., ZHANG, C., DU, W. An analysis on the crisis of "Chips shortage" in automobile industry — based on the double influence of COVID-19 and trade friction. Journal of Physics: Conference Series, 2021, vol. 1971, p. 1–5. DOI: 10.1088/1742-6596/1971/1/012100
2. RABAEY, J. Low Power Design Essentials. 1st ed. Springer US, 2009. ISBN: 9780387717135
3. HOROWITZ, M., ALON, E., PATIL, D., et al. Scaling, power, and the future of CMOS. In IEEE International Electron Devices Meeting (IEDM Technical Digest). Washington (DC, USA), 2005, p. 7–15. DOI: 10.1109/IEDM.2005.1609253
4. ROY, K., MUKOPADHYAY, S., MAHMOODI-MEIMAND, H. Leakage current mechanisms and leakage reduction techniques in deep-submicrometer CMOS circuits. Proceedings of the IEEE, 2000, vol. 88, no. 4, p. 305–327. DOI: 10.1109/JPROC.2002.808156
5. SINGH, K., ROSAS, O. A. R., JIAO, H., et al. Multi-bit pulsedlatch based low power synchronous circuit design. In IEEE International Symposium on Circuits and Systems (ISCAS). Florence (Italy), 2018, p. 1–5. DOI: 10.1109/ISCAS.2018.8351251
6. ROSAS, O. A. R. Multi-bit Pulse-based Latches for Low Power Design. Master’s Thesis. Eindhoven University of Technology, 2017.
7. WESTE, N. H. E., HARRIS, D. M. CMOS VLSI Design: A Circuits and Systems Perspective. 4th ed. Boston: Addison Wesley, 2010. ISBN: 0-321-54774-8
8. RABAEY, J. M., CHANDRAKASAN, A., NIKOLIĆ, B. Digital Integrated Circuits: A Design Perspective. 2nd ed. Upper Saddle River: Pearson, 2003. ISBN: 0130909963
9. KAESLIN, H. Digital Integrated Circuit Design: From VLSI Architectures to CMOS Fabrication. Cambridge: Cambridge University Press, 2008. ISBN: 978-0-521-88267-5
10. KANG, S. M., LEBLEBICI, Y. CMOS Digital Integrated Circuits: Analysis and Design. 3rd ed. McGraw-Hill, 2003. ISBN: 9780072460537
11. NILSSON, P. Arithmetic reduction of the static power consumption in nanoscale CMOS. In 13th IEEE International Conference on Electronics, Circuits and Systems. Nice (France), 2006, p. 656–659. DOI: 10.1109/ICECS.2006.379874
12. KAO, J., NARENDRA, S., CHANDRAKASAN, A. Subthreshold leakage modeling and reduction techniques [IC CAD tools]. In IEEE/ACM International Conference on Computer Aided Design (ICCAD 2002). San Jose (CA, USA), 2002, p. 141–148. DOI: 10.1109/ICCAD.2002.1167526
13. RAZAVI, B. Fundamentals of Microelectronics. 2nd ed. Wiley, 2013. ISBN: 978-1118156322
14. HEO, S., KRASHINSKY, R., ASANOVIC, K. Activity-sensitive flip-flop and latch selection for reduced energy. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2007, vol. 15, no. 9, p. 1060–1064. DOI: 10.1109/TVLSI.2007.902211
15. JACOB BAKER, R. CMOS Circuit Design, Layout, and Simulation. 3rd ed. Hoboken: Wiley-IEEE Press, 2010. ISBN: 978-0-470-88132-3
16. KHANNA, V. K. Integrated Nanoelectronics. Nanoscale CMOS, Post-CMOS and Allied Nanotechnologies. New Delhi (India): Springer, 2016. ISBN: 978-81-322-3623-8
17. ROY, K., PRASAD, S. Low-Power CMOS VLSI Circuit Design. New York: John Wiley, 2000. ISBN: 0-471-11488-X
18. ELSHARKASY, W. M. Low Power Reliable Design using Pulsed Latch Circuits. Dissertation. University of California, Irvine, 2017. [Online] Cited 2023-07-18. Available at: https://escholarship.org/uc/item/5ss2z430

Keywords: 5G chips, area-friendly design, automotive, consumer flip-flops, digital standard cell, dynamic power, leakage, low power chips, multi-bit pulsed latch, pulsed latch, saving area, scan mode, serial shifter, static power

M. Tatovic, P. B. Petrovic [references] [full-text] [DOI: 10.13164/re.2023.0568] [Download Citations]
Single Active Block-Based Emulators for Electronically Controllable Floating Meminductors and Memcapacitors

This paper introduces two novel emulator circuits that employ a single active block. The first circuit utilizes a Voltage Differencing Transconductance Amplifier (VDTA) to emulate the behavior of a floating/grounded incremental/decremental flux-controlled meminductor. The second circuit, based on a Voltage Differencing Current Conveyor (VDCC), emulates the characteristics of memcapacitance. Both emulation circuits are constructed using capacitors as the only type of grounded passive element. Notably, these circuits possess electronic tunability, enabling control over the realized inverse meminductance/memcapacitance. The theoretical analysis of the proposed emulators includes an investigation into potential non-idealities and parasitic effects. By carefully selecting the passive circuit elements, efforts were made to minimize the impact of these unwanted effects. In comparison to existing designs documented in the literature, the proposed circuits demonstrate remarkable simplicity. Additionally, they exhibit wide frequency operability (up to 50 MHz) and successfully pass the non-volatility test. Simulation results conducted using 0.18 μm CMOS technology and a ±0.9 V supply voltage align closely with the theoretical predictions. Furthermore, Monte Carlo simulations and corner analysis are employed to evaluate the circuit's robustness. To validate the feasibility of the proposed solution, experimental tests are performed using commercially available components.

1. CHUA, L. O. Memristor – The missing circuit element. IEEE Transactions on Circuit Theory, 1971, vol. 18, no. 5, p. 507–519. DOI: 10.1109/TCT.1971.1083337
2. CHUA, L. O. Device modeling via nonlinear circuit elements. IEEE Transactions on Circuits and Systems, 1980, vol. 27, no. 11, p. 1014–1044. DOI: 10.1109/TCS.1980.1084742
3. CHUA, L. O., KANG, S. M. Memristive devices and systems. Proceedings of the IEEE, 1976, vol. 64, no. 2, p. 209–223. DOI: 10.1109/PROC.1976.10092
4. DI VENTRA, M., PERSHIN, Y.V., CHUA, L. O. Circuit elements with memory: Memristors, memcapacitors, and meminductors. Proceedings of the IEEE, 2009, vol. 97, no. 10, p. 1717–1724. DOI: 10.1109/JPROC.2009.2021077
5. KHALIL, N. A., FOUDA, F. E., SAID, L. A., et al. A general emulator for fractional-order memristive elements with multiple pinched points and application. AEU - International Journal of Electronics and Communications, 2020, vol. 124, p. 1–15. DOI: 10.1016/j.aeue.2020.153338
6. BIOLEK, D., BIOLEK, Z., BIOLKOVA, V. SPICE modeling of memristive, memcapacitative and meminductive systems. In 2009 European Conference on Circuit Theory and Design. Antalya (Turkey), 2009, p. 249–252. DOI: 10.1109/ECCTD.2009.5274934
7. BIOLEK, D., BIOLEK, Z., BIOLKOVA, V. Behavioral modeling of memcapacitor. Radioengineering, 2011, vol. 20, no. 1, p. 228 to 233. ISSN: 1210-2512
8. BIOLEK, D., BIOLEK, Z., BIOLKOVA, V. PSPICE modeling of meminductor. Analog Integrated Circuits and Signal Processing, 2011, vol. 66, no. 1, p. 129–137. DOI: 10.1007/s10470-010-9505-5
9. ÇAM TAŞKIRAN, Z. G., SAĞBAŞ, M., AYTEN, U. E., et al. A new universal mutator circuit for memcapacitor and meminductor elements. AEU - International Journal of Electronics and Communications, 2020, vol. 119, p. 1–11. DOI: 10.1016/j.aeue.2020.153180
10. SINGH, A., RAI, S. K. VDCC-based memcapacitor/meminductor emulator and its application in adaptive learning circuit. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2021, vol. 45, no. 4, p. 1151–1163. DOI: 10.1007/s40998-021-00440-x
11. RAJ, N., RANJAN, R. K., KHATEB, F., et al. Mem-elements emulator design with experimental validation and its application. IEEE Access, 2021, vol. 9, p. 69860–69875. DOI: 10.1109/ACCESS.2021.3078189
12. VISTA, J., RANJAN, A. High frequency meminductor emulator employing VDTA and its application. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, vol. 39, no. 10, p. 2020–2028. DOI: 10.1109/TCAD.2019.2950376
13. KONAL, M., KACAR, F. Electronically tunable meminductor based on OTA. AEUE - International Journal of Electronics and Communications, 2020, vol. 126, p. 1–9. DOI: 10.1016/j.aeue.2020.153391
14. RAJ, A., KUMAR, K., KUMAR, P. CMOS realization of OTA based tunable grounded meminductor. Analog Integrated Circuits and Signal Processing, 2021, vol. 107, no. 2, p. 475–482. DOI: 10.1007/s10470-021-01808-z
15. KUMAR, K., NAGAR, B. C. New tunable resistorless grounded meminductor emulator. Journal of Computational Electronics, 2021, vol. 20, no. 3, p. 1452–1460. DOI: 10.1007/s10825-021-01697-5
16. BHARDWAJ, K., SRIVASTAVA, M. New electronically adjustable memelement emulator for realizing the behaviour of fully-floating meminductor and memristor. Microelectronics Journal, 2021, vol. 114, p. 1–15. DOI: 10.1016/j.mejo.2021.105126
17. ORMAN, K., YESIL, A., BABACAN, Y. DDCC-based meminductor circuit with hard and smooth switching behaviors and its circuit implementation. Microelectronics Journal, 2022, vol. 125, p. 1–7. DOI: 10.1016/j.mejo.2022.105462
18. YADAV, N., RAI, S. K, PANDEY, R. New high frequency memristorless and resistorless meminductor emulators using OTA and CDBA. Sadhana, 2022, vol. 47, no. 1, p. 1–18. DOI: 10.1007/s12046-021-01785-z
19. BHARDWAJ, K., SRIVASTAVA, M. New grounded passive elements-based external multiplier-less memelement emulator to realize the floating meminductor and memristor. Analog Integrated Circuits and Signal Processing, 2022, vol. 27, p. 1–21. DOI: 10.1007/s10470-021-01976-y
20. PETROVIC, P. B. A new electronically controlled floating/grounded meminductor emulator based on single MOVDTA. Analog Integrated Circuits and Signal Processing, 2022, vol. 110, no. 1, p. 185–195. DOI: 10.1007/s10470-021-01946-4
21. BHARDWAJ, K., SRIVASTAVA, M. Compact floating dual memelement emulator employing VDIBA and OTA: A novel realization. Circuits, Systems and Signal Processing, 2022, vol. 41, p. 5933–5967. DOI: 0.1007/s00034-022-02067-7
22. SOZEN, H., CAM, U. A novel floating/grounded meminductor emulator. Journal of Circuits, Systems and Computers, 2020, vol. 29, no. 15. DOI: 10.1142/S0218126620502473
23. KORKMAZ, M. O., BABACAN, Y., YESIL, A. A new CCII based meminductor emulator circuit and its experimental results. AEU - International Journal of Electronics and Communications, 2023, vol. 158, p. 1–9. DOI: 10.1016/j.aeue.2022.154450
24. VISTA, J., RANJAN, A. Simple charge controlled floating memcapacitor emulator using DXCCDITA. Analog Integrated Circuits and Signal Processing, 2020, vol. 104, no. 1, p. 37–46. DOI: 10.1007/s10470-020-01650-9
25. YU, D., ZHAO, X., SUN, T., et al. A simple floating mutator for emulating memristor, memcapacitor, and meminductor. IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, vol. 67, no. 7, p. 1334–1338. DOI: 10.1109/TCSII.2019.2936453
26. SHARMA, P. K., RANJAN, R. K., KHATEB, F., et al. Charged controlled mem-element emulator and its application in a chaotic system. IEEE Access, 2020, vol. 8, p. 171397–171407. DOI: 10.1109/ACCESS.2020.3024769
27. KONAL, M., KACAR, F., BABACAN, Y. Electronically controllable memcapacitor emulator employing VDCCs. AEU - International Journal of Electronics and Communications, 2021, vol. 140, p. 1–7. DOI: 10.1016/j.aeue.2021.153932
28. YESIL, A., BABACAN, Y. Electronically controllable memcapacitor circuit with experimental results. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, vol. 68, no. 4, p. 1443–1447. DOI: 10.1109/TCSII.2020.3030114
29. HOSBAS, M. Z., KAÇAR, F., YESIL, A. Memcapacitor emulator using VDTA-memristor. Analog Integrated Circuits and Signal Processing, 2022, vol. 110, no. 2, p. 361–370. DOI: 10.1007/s10470-021-01974-0
30. ANANDA, Y. R., SATYANARAYAN, G. S., TRIVEDI, G. An optimized MOS-based high frequency charge controlled memcapacitor emulator. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2022, vol. 12, no. 4, p. 793–803. DOI: 10.1109/JETCAS.2022.3221314
31. PETROVIC, P. B. Electronically adjustable grounded memcapacitor emulator based on single active component with variable switching mechanism. Electronics, 2022, vol. 11, no. 1, p. 1–15. DOI: 10.3390/electronics11010161
32. KORKMAZ, M. O., YESIL, A. FDCCII based new memcapacitor emulator circuit with electronically tunable. Muhendislik Bilimleri ve Araştırmaları Dergisi, 2023, vol. 5, no. 1, p. 127–134. DOI: 10.46387/bjesr.1259980
33. ANANDA, Y. R., SATYANARAYAN, G. S., TRIVEDI, G. A high frequency MOS-based floating charge controlled memcapacitor emulator. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 3, p. 1189–1193. DOI: 10.1109/TCSII.2022.3221334
34. BHARDWAJ, K., SRIVASTAVA, M. New multiplier-less compact tunable charge-controlled memelement emulator using grounded passive elements. Circuits, Systems and Signal Processing, 2022, vol. 41, p. 2429–2465. DOI: 0.1007/s00034-021-01895-3
35. PETROVIĆ, P. B. A universal electronically controllable memelement emulator based on VDCC with variable configuration. Electronics, 2022, vol. 11, p. 1–23. DOI: 10.3390/electronics11233957
36. ARBEL, A. F., GOLDMINZ, L. Output stage for current-mode feedback amplifiers, theory and applications. Analog Integrated Circuits and Signal Processing, 1992, vol. 3, p. 243–255. DOI: 10.1007/BF00276637
37. BHARDWAJ, K., SRIVASTAVA, M. Compact charge-controlled memristance simulator with electronic/resistive tunability. Journal of Circuits, Systems, and Computers, 2022, vol. 31, no. 5, p 1–21. DOI: 10.1142/S0218126622500943
38. PETROVIĆ, P. B. Charge-controlled grounded memristor emulator circuits based on Arbel-Goldminz cell with variable switching behaviour. Analog Integrated Circuits and Signal Processing, 2022, vol. 113, p. 373–381. DOI: 10.1007/s10470-022-02042-x
39. PETROVIĆ, P. B. Single VDTA-based lossless and lossy electronically tunable positive and negative grounded capacitance multipliers. Circuits, Systems and Signal Processing, 2022, vol. 41, p. 6581–6614. DOI: 0.1007/s00034-022-02094-4
40. COOKE, S. F., BLISS, T. V. P. Plasticity in the human central nervous system. Brain, 2006, vol. 129, p. 1659–1673. DOI: 10.1093/brain/awl082

Keywords: Meminductor, memcapacitor, emulator, VDTA, VDCC, grounded passive components, electronic controller, simulation

S. Meng, C. Meng, C. Wang, Q. Wang [references] [full-text] [DOI: 10.13164/re.2023.0583] [Download Citations]
Optimization of Bipolar Toeplitz Measurement Matrix Based on Cosine-Exponential Chaotic Map and Improved Abolghasemi Algorithm

In compressive sensing theory, the measurement matrix plays a crucial role in compressive observation of sparse signals. The bipolar Toeplitz measurement matrix constructed based on chaotic map has advantages such as generating fewer free elements and supporting fast algorithms, making it widely used. While optimizing the measurement matrix can effectively improve its compressive sensing reconstruction performance, existing optimization algorithms are not suitable for the bipolar Toeplitz measurement matrix due to its structural and bipolar properties. To address this issue, this paper proposes an optimization method for the bipolar Toeplitz measurement matrix based on cosine-exponential (CE) chaotic map sequences and an improved Abolghasemi algorithm. Using an enhanced CE chaotic map to generate chaotic sequences with greater chaos and randomness, we construct the measurement matrix and optimize it using the structure matrix and the improved Abolghasemi algorithm, which preserves the matrix's bipolarity without altering its structure. We also introduce constraints on the generated sequence values during the optimization process. Through simulation experiments, the effectiveness of our optimization algorithm is verified, as the optimized bipolar Toeplitz measurement matrix significantly reduces reconstruction error and improves reconstruction probability.

1. CANDES, E. J. Compressive sampling. In International Congress of Mathematicians. Madrid (Spain), 2007. Vol. III (Invited Lectures), p. 1433–1452. DOI: 10.4171/022-3/69
2. CANDES, E. J., ROMBERG, J. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 2006, vol. 6, p. 227–254. DOI: 10.1007/s10208-004-0162-x
3. CANDES, E. J., ROMBERG, J., TAO, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, vol. 52, no. 2, p. 489–509. DOI: 10.1109/TIT.2005.862083
4. CANDES, E. J., ROMBERG, J., TAO, T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, vol. 59, no. 8, p. 1207 to 1223. DOI: 10.1002/cpa.20124
5. CANDES, E. J., TAO, T. Near-optimal signal recovery from random projections: Universal encoding strategies. IEEE Transactions on Information Theory, 2006, vol. 52, no. 12, p. 5406–5425. DOI: 10.1109/TIT.2006.885507
6. DONOHO, D. L. Compressed sensing. IEEE Transactions on Information Theory, 2006, vol. 52, no. 4, p. 1289–1306. DOI: 10.1109/TIT.2006.871582
7. AJAMIAN, T., MOUSSAOUI, S., DUPRET, A., et al. Compressed signal acquisition in wire diagnostic. In IEEE Sensors. Glasgow (UK), 2017, p. 1–3. DOI: 10.1109/ICSENS.2017.8234017
8. AMBROSANIO, M., KOSMAS, P., PASCAZIO, V. A multithreshold iterative DBIM-based algorithm for the imaging of heterogeneous breast tissues. IEEE Transactions on Biomedical Engineering, 2019, vol. 66, no. 2, p. 509–520. DOI: 10.1109/TBME.2018.2849648
9. WANG, Q., MENG, C., WANG, C. Compressive sampling and reconstruction for LFM signals with unknown modulating rate based on Gabor space (in Chinese). Journal of Signal Processing, 2022, vol. 38, no. 4, p. 747–758. DOI: 10.16798/j.issn.1003-0530.2022.04.009
10. CANDES, E. J., TAO, T. Decoding by linear programming. IEEE Transactions on Information Theory, 2005, vol. 51, no. 12, p. 4203-4215. DOI: 10.1109/TIT.2005.858979
11. BARANIUK, R., DAVENPORT, M., DEVORE, R., et al. A simple proof of the restricted isometry property for random matrices. Constructive Approximation, 2008, vol. 28, no. 3, p. 253–263. DOI: 10.1007/s00365-007-9003-x
12. BAI, H., LI, G., LI, S., et al. Alternating optimization of sensing matrix and sparsifying dictionary for compressed sensing. IEEE Transactions on Signal Processing, 2015, vol. 63, no. 6, p. 1581–1594. DOI: 10.1109/TSP.2015.2399864
13. CHEN, Y. J., ZHANG, Q., LUO, Y., et al. Measurement matrix optimization for ISAR sparse imaging based on genetic algorithm. IEEE Geoscience and Remote Sensing Letters, 2016, vol. 13, no. 12, p. 1875–1879. DOI: 10.1109/LGRS.2016.2616352
14. RAGHEB, T., LASKA, J. N., NEJATI, H., et al. A prototype hardware for random demodulation based compressive analog-to-digital conversion. In 2008 51st Midwest Symposium on Circuits and Systems. Knoxville (TN, USA), 2008, p. 37–40. DOI:10.1109/MWSCAS.2008.4616730
15. ARIE, R., BRAND, A., ENGELBERG, S. Compressive sensing and sub-Nyquist sampling. IEEE Instrumentation and Measurement Magazine, 2020, vol. 23, no. 2, p. 94–101. DOI: 10.1109/MIM.2020.9062696
16. YU, L., BARBOT, J. P., ZHENG, G., et al. Compressive sensing with chaotic sequence. IEEE Signal Processing Letters, 2010, vol. 17, no. 8, p. 731–734. DOI: 10.1109/LSP.2010.2052243
17. YI, R., CUI, C., MIAO, Y., et al. A method of constructing measurement matrix for compressed sensing by Chebyshev chaotic sequence. Entropy, 2020, vol. 22, no. 10, p. 1–16. DOI: 10.3390/e22101085
18. BENAZZOUZA, S., RIDOUANI, M., SALAHDINE, F., et al. Chaotic compressive spectrum sensing based on Chebyshev map for cognitive radio networks. Symmetry, 2021, vol. 13, p. 1–22. DOI: 10.3390/sym13030429
19. ZHANG, Y. The unified image encryption algorithm based on chaos and cubic S-Box. Information Sciences, 2018, vol. 450, p. 361–377. DOI: 10.1016/j.ins.2018.03.055
20. KAMEL, S. H., ABD-EL-MALEK, M. B., EL-KHAMY, S. E. Compressive spectrum sensing using chaotic matrices for cognitive radio networks. International Journal of Communication Systems, 2019, vol. 32, no. 6, p. 1–16. DOI: 10.1002/dac.3899
21. VLAD, A., LUCA, A., FRUNZETE, M. Computational measurements of the transient time and of the sampling distance that enables statistical independence in the logistic map. In International Conference on Computational Science and Its Applications (ICCSA 2009). Seoul (Korea), 2009, vol. 2, p. 703 to 718. DOI: 10.1007/978-3-642-02457-3_59
22. VADUVA, A., VLAD, A., BADEA, B. Evaluating the performance of a test-method for statistical independence decision in the context of chaotic signals. In 2016 International Conference on Communications (COMM). Bucharest (Romania), 2016, p. 417 to 422. DOI: 10.1109/ICComm.2016.7528207
23. GAN, H., ZHANG, T., HUA, Y., et al. Toeplitz-block sensing matrix based on bipolar chaotic sequence (in Chinese). Acta Physica Sinica -Chinese Edition, 2021, vol. 70, no. 3, p. 1–12.
24. ELAD, M. Optimized projections for compressed sensing. IEEE Transactions on Signal Processing, 2007, vol. 55, no. 12, p. 5695 to 5702. DOI: 10.1109/TSP.2007.900760
25. DUARTE-CARVAJALINO, J. M., SAPIRO, G. Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Transactions on Image Processing, 2009, vol. 18, no. 7, p. 1395–1408. DOI: 10.1109/TIP.2009.2022459
26. ABOLGHASEMI, V., FERDOWSI, S., SANEI, S. A gradientbased alternating minimization approach for optimization of the measurement matrix in compressive sensing. Signal Processing, 2012, vol. 92, no. 4, p. 999–1009. DOI: 10.1016/j.sigpro.2011.10.012
27. WICKER, S. B., KIM, S. Low-density parity-check codes. In Fundamentals of Codes, Graphs, and Iterative Decoding. The International Series in Engineering and Computer Science, vol. 714, ch. 8, p. 137–175. DOI: 10.1007/0-306-47794-7_8
28. ATIF, S. M., QAZI, S., GILLIS, N. Improved SVD-based initialization for nonnegative matrix factorization using low-rank correction. Pattern Recognition Letters, 2019, vol. 122, p. 53–59. DOI: 10.1016/j.patrec.2019.02.018

Keywords: Chaotic map, measurement matrix, bipolar Toeplitz matrix, optimization

P. Kavitha, K. Kavitha [references] [full-text] [DOI: 10.13164/re.2023.0594] [Download Citations]
Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

Wireless Federated Learning (WFL) is an innovative machine learning paradigm enabling distributed devices to collaboratively learn without sharing raw data. WFL is particularly useful for mobile devices that generate massive amounts of data but have limited resources for training complex models. This paper highlights the significance of reducing delay for efficient WFL implementation through advanced multiple access protocols and joint optimization of communication and computing resources. We propose optimizing the WFL Compute-then-Transmit (CT) protocol using hybrid Non-Orthogonal Multiple Access (H-NOMA). To minimize and optimize latency for the transmission of local training data, we use the Successive Convex Optimization (SCA) method, which efficiently reduces the complexity of non-convex algorithms. Finally, the numerical results verify the effectiveness of H-NOMA in terms of delay reduction, compared to the benchmark that is based on Non-Orthogonal Multiple Acces (NOMA).

1. MCMAHAN, B. H., MOORE, E., RAMAGE, D., et al. Communication-efficient learning of deep networks from decentralized data. In 20th International Conference on Artificial Intelligence and Statistics (AISTATS). Ft. Lauderdale (FL, USA), 2017, p. 1273–1282. DOI: 10.48550/arXiv:1602.05629
2. CHEN, M., YANG, Z., SAAD, W., et al. A joint learning and communications framework for federated learning over wireless networks. IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 1, p. 269–283. DOI: 10.1109/TWC.2020.3024629
3. YANG, Z., CHEN, M., WONG, K. K., et al. Federated learning for 6G: Applications, challenges, and opportunities. Engineering, 2022, vol. 8, p. 33–41. DOI: 10.1016/j.eng.2021.12.002
4. LIU, Y., YUAN, X., XIONG, Z., et al. Federated learning for 6G communications: Challenges, methods, and future directions. China Communications, 2020, vol. 17, no. 9, p. 105–118. DOI: 10.23919/JCC.2020.09.009
5. LI, T., SAHU, K., TALWALKAR, A., et al. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 2020, vol. 37, no. 3, p. 50–60. DOI: 10.1109/MSP.2020.2975749
6. KONECNY, J., MCMAHAN, H. B., YU, F. X., et al. Federated learning: Strategies for improving communication efficiency. arXiv, 2016, p. 1–10. DOI: 10.48550/arXiv.1610.05492
7. KAIROUZ, P., MCMAHAN, H. B., AVENT, B., et al. Advances and Open Problems in Federated Learning. Now Publishers, 2021. ISBN: 9781680837889
8. CHEN, D., XIE, L. J., KIM, B., et al. Federated learning-based mobile edge computing for augmented reality applications. In Proceedings of International Conference on Computing, Networking and Communications (ICNC). Big Island (HI, USA), 2020, p. 767–773. DOI: 10.1109/ICNC47757.2020.9049708
9. KANG, J., XIONG, Z., NIYATO, D., et al. Reliable federated learning for mobile networks. IEEE Wireless Communications, 2020, vol. 27, no. 2, p. 72–80. DOI: 10.1109/MWC.001.1900119
10. LIU, Y., PENG, J., KANG, J., et al. A secure federated learning framework for 5G networks. IEEE Wireless Communications, 2020, vol. 27, no. 4, p. 24–31. DOI: 10.1109/MWC.01.1900525
11. LIM, W. Y. B., LUONG, N. C., HOANG, D. T., et al. Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 2020, vol. 22, no. 3, p. 2031–2063. DOI: 10.1109/COMST.2020.2986024
12. NGUYEN, D. C., DING, M., PHAM, Q., et al. Federated learning meets blockchain in edge computing: Opportunities and challenges. IEEE Internet of Things Journal, 2021, vol. 8, no. 16, p. 12806–12825. DOI: 10.1109/JIOT.2021.3072611
13. CHEN, M., GUNDUZ, D., HUANG, K., et al. Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications, 2021, vol. 39, no. 12, p. 3579–3605. DOI: 10.1109/JSAC.2021.3118346
14. LETAIEF, K. B., CHEN, W., SHI, Y., et al. The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 2019, vol. 57, no. 8, p. 84–90. DOI: 10.1109/MCOM.2019.1900271
15. BOUZINIS, P. S., DIAMANTOULAKIS, P. D., KARAGIANNIDIS, G. K. Wireless federated learning (WFL) for 6G networks part I: Research challenges and future trends. IEEE Communications Letters, 2022, vol. 26, no. 1, p. 3–7. DOI: 10.1109/LCOMM.2021.3121071
16. DING, Z., LEI, X., KARAGIANNIDIS, G. K., et al. A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends. IEEE Journal on Selected Areas in Communications, 2017, vol. 35, no. 10, p. 2181–2195. DOI: 10.1109/JSAC.2017.2725519
17. LIU, Y., ZHANG, S., MU, X., et al. Evolution of NOMA toward next generation multiple access (NGMA) for 6G. IEEE Journal on Selected Areas in Communications, 2022, vol. 40, no. 4, p. 1037–1071. DOI: 10.1109/JSAC.2022.3145234
18. 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
19. DING, Z., FAN, P., POOR, H. V. Impact of non-orthogonal multiple access on the offloading of mobile edge computing. IEEE Transactions on Communications, 2019, vol. 67, no. 1, p. 375–390. DOI: 10.1109/TCOMM.2018.2870894
20. ZENG, M., NGUYEN, N. P., DOBRE, O. A., et al. Delay minimization for NOMA-assisted MEC under power and energy constraints. IEEE Wireless Communications Letters, 2019, vol. 8, no. 6, p. 1657–1661. DOI: 10.1109/LWC.2019.2934453
21. DING, Z., NG, D. W. K., SCHOBER, R., et al. Delay minimization for NOMA-MEC offloading. IEEE Signal Processing Letters, 2018, vol. 25, no. 12, p. 1875–1879. DOI: 10.1109/LSP.2018.2876019
22. BUDHIRAJA, I., KUMAR, N., TYAGI, S., et al. Energy consumption minimization scheme for NOMA-based mobile edge computation networks underlying UAV. IEEE Systems Journal, 2021, vol. 15, no. 4, p. 5724–5733. DOI: 10.1109/JSYST.2021.3076782
23. SU, B., NI, Q., YU, W., et al. Optimizing computation efficiency for NOMA-assisted mobile edge computing with user cooperation. IEEE Transactions on Green Communications and Networking, 2021, vol. 5, no. 2, p. 858–867. DOI: 10.1109/TGCN.2021.3056770
24. BOUZINIS, P. S., DIAMANTOULAKIS, P. D., KARAGIANNIDIS, G. K. Wireless federated learning (WFL) for 6G networks part II: The compute-then-transmit NOMA paradigm. IEEE Communications Letters, 2022, vol. 26, no. 1, p. 8–12. DOI: 10.1109/LCOMM.2021.3121067
25. DING, Z., XU, D., SCHOBER, R., et al. Hybrid NOMA offloading in multi-user MEC networks. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 7, p. 5377–5391. DOI: 10.1109/TWC.2021.3139932
26. DURSUN, Y., FANG, F., DING, Z. Hybrid NOMA based MIMO offloading for mobile edge computing in 6G networks. China Communications, 2022, vol. 19, no. 10, p. 12–20. DOI: 10.23919/JCC.2022.00.024
27. DURSUN, Y., GOKTAS, M. B., DING, Z. Green NOMA based MUMIMO transmission for MEC in 6G Networks. Computer Networks, 2023, vol. 228, p. 1–7. DOI: 10.1016/j.comnet.2023.109749

Keywords: WFL, NOMA, SCA, latency, Compute-then-Transmit (CT)

L. Tan, J. Liu, Y. Zhou, R. Chen [references] [full-text] [DOI: 10.13164/re.2023.0603] [Download Citations]
Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain

Coverless image steganography typically extracts feature sequences from cover images to map information. Once the extracted features have high similarity, it is challenging to construct a complete mapping sequence set, which places a heavy burden on the underlying storage and computation. In order to improve database utilization while increasing the data-hiding capacity, we propose a coverless steganography model based on low-similarity feature selection in the DCT domain. A mapping algorithm is presented based on an 8000-dimensional feature termed CS-DCTR extracted from each image to convert into binary sequences. The high feature dimension leads to a high capacity, ranging from 8 to 25 bits per image. Furthermore, scrambling is employed for feature mapping before building an inverted index tree, considerably enhancing security against steganalysis. Experimental results show that CS-DCTR features exhibit high diversity, averaging 49.3% complete mapping sequences, which indicates lower similarity among CS-DCTR features. The technique also demonstrates resistance to normal operations and benign attacks. The information extraction accuracy rises to 96.7% on average under typical noise attacks. Moreover, our technique achieves excellent performance in terms of hiding capacity, image utilization, and transmission security.

1. YANG, C. H., WENG, C. Y., WANG, S. J., et al. Adaptive data hiding in edge areas of images with spatial LSB domain systems. IEEE Transactions on Information Forensics and Security, 2008, vol. 3, no. 3, p. 488–497. DOI: 10.1109/TIFS.2008.926097
2. MCKEON, R. T. Strange Fourier steganography in movies. In 2007 IEEE International Conference on Electro/Information Technology. Chicago (IL, USA), 2007, p. 178–182. DOI: 10.1109/EIT.2007.4374540
3. VALANDAR, M. Y., AYUBI, P., BARANI, M. J. A new transform domain steganography based on modified logistic chaotic map for color images. Journal of Information Security and Applications, 2017, vol. 34, no. 2, p. 142–151. DOI: 10.1016/j.jisa.2017.04.004
4. COX, I. J., KILIAN, J., LEIGHTON, T., et al. Secure spread spectrum watermarking for images, audio and video. In Proceedings of 3rd IEEE international conference on image processing. Lausanne (Switzerland), 1996, vol. 3, p. 243–246. DOI: 10.1109/ICIP.1996.560429
5. LIN, W. H., HORNG, S. J., KAO, T. W., et al. An efficient watermarking method based on significant difference of wavelet coefficient quantization. IEEE Transactions on Multimedia, 2008, vol. 10, no. 5, p. 746–757. DOI: 10.1109/TMM.2008.922795
6. JANA, B. High payload reversible data hiding scheme using weighted matrix. Optik - International Journal for Light and Electron Optics, 2016, vol. 127, no. 6, p. 3347–3358. DOI: 10.1016/j.ijleo.2015.12.055
7. MUKHERJEE, S., JANA, B. A novel method for high capacity reversible data hiding scheme using difference expansion. International Journal of Natural Computing Research, 2019, vol. 8, no. 4, p. 1–27. DOI: 10.4018/IJNCR.2019100102
8. KODOVSKY, J., FRIDRICH, J., HOLUB, V. Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security, 2011, vol. 7, no. 2, p. 432–444. DOI: 10.1109/TIFS.2011.2175919
9. HOLUB, V., FRIDRICH, J. Phase-aware projection model for steganalysis of jpeg images. Media Watermarking, Security, and Forensics, 2015, vol. 9409, p. 259–269. DOI: 10.1117/12.2075239
10. FRIDRICH, J., KODOVSKY, J. Rich models for steganalysis of digital images. IEEE Transactions on information Forensics and Security, 2012, vol. 7, no. 3, p. 868–882. DOI: 10.1109/TIFS.2012.2190402
11. XU, G., WU, H. Z., SHI, Y. Q. Structural design of convolutional neural networks for steganalysis. IEEE Signal Processing Letters, 2016, vol. 23, no. 5, p. 708–712. DOI: 10.1109/LSP.2016.2548421
12. BOROUMAND, M., CHEN, M., FRIDRICH, J. Deep residual network for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 2018, vol. 14, no. 5, p. 1181–1193. DOI: 10.1109/TIFS.2018.2871749
13. ZHOU, Z., SUN, H., HARIT, R., et al. Coverless image steganography without embedding. In International Conference on Cloud Computing and Security (ICCCS). Nanjing (China), 2015, p. 123–132. DOI: 10.1007/978-3-319-27051-7_11
14. CAO, Y., ZHOU, Z. L., SUN, X. M. Coverless information hiding based on bag-of-words model of image. Journal of Applied Sciences, 2016, vol. 34, no. 5, p. 527–536. DOI: 10.3970/cmc.2018.054.197
15. ZHENG, S., LIANG, W., LING, B., et al. Coverless information hiding based on robust image hashing. In International Conference on Intelligent Computing (ICICCS). Madurai (India), 2017, p. 536–547. DOI: 10.1007/978-3-319-63315-2_47
16. LOWE, D. G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, vol. 60, p. 91–110. DOI: 10.1023/B:VISI.0000029664.99615.94
17. CAO, Y., ZHOU, Z. L., SUN, X. M., et al. Coverless information hiding based on the molecular structure images of material. Computers, Materials & Continua, 2018, vol. 54, no. 2, p. 197–207. DOI: 10.3970/cmc.2018.054.197
18. ZHANG, X., PENG, F., LONG, M. Robust coverless image steganography based on DCT and LDA topic classification. IEEE Transactions on Multimedia, 2018, vol. 20, no. 12, p. 3223–3238. DOI: 10.1109/TMM.2018.2838334
19. LIU, Q., XIANG, X., QIN, J., et al. Coverless steganography based on image retrieval of densenet features and DWT sequence mapping. Knowledge-Based Systems, 2019, vol. 192, p. 1–15. DOI: 10.1016/j.knosys.2019.105375
20. LUO, Y., QIN, J., XIANG, X., et al. Coverless image steganography based on multi-object recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2020, vol. 31, no. 7, p. 2779–2791. DOI: 10.1109/TCSVT.2020.3033945
21. ZHOU, Z., CAO, Y., WANG, M. Faster-RCNN based robust coverless information hiding system in cloud environment. IEEE Access, 2019, vol. 7, p. 1–7. DOI: 10.1109/ACCESS.2019.2955990
22. LUO, Y. J., QIN, J., XIANG, X., et al. Coverless image steganography based on image segmentation. Computers, Materials and Continua, 2020, vol. 64, no. 2, p. 1281–1295. DOI: 10.32604/cmc.2020.010867
23. LIU, Q., XIANG, X., QIN, J., et al. A robust coverless steganography scheme using camouflage image. IEEE Transactions on Circuits and Systems for Video Technology, 2021, vol. 32, no. 6, p. 1–14. DOI: 10.1109/TCSVT.2021.3108772
24. LIU, M. M., ZHANG, M. Q., LIU, J., et al. Coverless information hiding based on generative adversarial networks. arXiv, 2017, p. 1–14. DOI: 10.48550/arXiv.1712.06951
25. HU, D., WANG, L., JIANG, W., et al. A novel image steganography method via deep convolutional generative adversarial networks. IEEE Access, 2018, vol. 6, p. 38303–38314. DOI: 10.1109/ACCESS.2018.2852771
26. DI, F., LIU, J., ZHANG, Z., et al. Somewhat reversible data hiding by image to image translation. arXiv, 2019, p. 1–16. DOI: 10.48550/arXiv.1905.02872
27. CHEN, X., ZHANG, Z., QIU, A., et al. A novel coverless steganography method based on image selection and stargan. IEEE Transactions on Network Science and Engineering, 2020, vol. 9, no. 1, p. 1–12. DOI: 10.1109/TNSE.2020.3041529
28. CHOI, Y., CHOI, M., KIM, M., et al. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City (UT, USA), 2018, p. 8789–8797. DOI: 10.1109/CVPR.2018.00916
29. HOLUB, V., FRIDRICH, J. Low-complexity features for jpeg steganalysis using undecimated DCT. IEEE Transactions on Information forensics and security, 2014, vol. 10, no. 2, p. 219–228. DOI: 10.1109/TIFS.2014.2364918
30. MA, S., ZHAO, X. Steganalytic feature based adversarial embedding for adaptive jpeg steganography. Journal of Visual Communication and Image Representation, 2021, vol. 76, no. 3, p. 1–12. DOI: 10.1016/j.jvcir.2021.103066
31. SEDIGHI, V., FRIDRICH, J. Histogram layer, moving convolutional neural networks towards feature-based steganalysis. Electronic Imaging, 2017, vol. 2017, no. 7, p. 50–55. DOI: 10.2352/ISSN.2470-1173.2017.7.MWSF-325

Keywords: Coverless, steganography, feature collision, DCTR, JPEG

T. Teng, A. He [references] [full-text] [DOI: 10.13164/re.2023.0616] [Download Citations]
Performance of Satellite UWOC Network with Generalized Boresight Error and AWGGN

This paper investigates a dual-hop satellite-marine communication network that employs mixed radio-frequency/underwater wireless optical communication (RF/UWOC). The study focuses on investigating the impacts of non-zero pointing errors and the additive white generalized Gaussian noise (AWGGN) on the dual-hop system. To address the challenge of computing the probability density function (PDF) for the UWOC system with non-zero boresight error, we apply the Laplace transformation and the generalized integro exponential function. Next, we utilize the generalized Gaussian noise to calculate the signal-to-noise ratio (SNR) and the conditional bit error rate (BER). Then, we present system performance metrics such as the outage probability (OP) and BER. We also calculate the asymptotic analysis of the OP and BER by considering poles coinciding, resulting in the proposal of four asymptotic formulas to gain additional insights into the diversity gain. Finally, we provide simulation results that analyze the performance of the proposed satellite-marine network with different system parameters, such as boresight displacements and bubble levels, and validate the accuracy of the numerical results.

1. SHIHADA, B. Aqua-Fi: Delivering internet underwater using wireless optical networks. IEEE Communications Magazine, 2020, vol. 58, no. 5, p. 84–89. DOI: 10.1109/mcom.001.2000009
2. WANG, C.-X., HUANG, J., WANG, H., et al. 6G wireless channel measurements and models: Trends and challenges. IEEE Vehicular Technology Magazine, 2020, vol. 15, no. 4, p. 22–32. DOI: 10.1109/mvt.2020.3018436
3. LI, S., YANG, L., DA COSTA, D. B., et al. Performance analysis of mixed RF-UWOC dual-hop transmission systems. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 11, p. 14043–14048. DOI: 10.1109/tvt.2020.3029529
4. LOU, Y., SUN, R., CHENG, J., et al. Secrecy outage analysis of two-hop decode-and-forward mixed RF/UWOC systems. IEEE Communications Letters, 2020, vol. 26, no. 5, p. 989–993. DOI: 10.1109/lcomm.2021.3058988
5. YADAV, S., VATS, A., AGGARWAL, M., et al. Performance analysis and altitude optimization of UAV-enabled dual-hop mixed RF-UWOC system. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 12, p. 12651–12661. DOI: 10.1109/tvt.2021.3118569
6. YANG, F., CHENG, J., A. TSIFTSIS, T. Free-space optical communication with nonzero boresight pointing errors. IEEE Transactions on Communications, 2014, vol. 62, no. 2, p. 713–725. DOI: 10.1109/tcomm.2014.010914.130249
7. UPADHYA, A., GUPTA, J., DWIVEDI, V. K., et al. Impact of RF I/Q imbalance on interference-limited mixed RF/FSO TWR systems with non-zero boresight error. IEEE Wireless Communications Letters, 2021, vol. 10, no. 2, p. 416–420. DOI: 10.1109/lwc.2020.3033528
8. WANG, Y., WANG, P., LIU, X., et al. On the performance of dual hop mixed RF/FSO wireless communication system in urban area over aggregated exponentiated Weibull fading channels with pointing errors. Optics Communications, 2018, vol. 410, p. 609–616. DOI: 10.1016/j.optcom.2017.10.062
9. MOHJAZI, L., BARIAH, L., MUHAIDAT, S., et al. Performance of reconfigurable intelligent surfaces in the presence of generalized Gaussian noise. IEEE Communications Letters, 2022, vol. 26, no. 4, p. 773–777. DOI: 10.1109/LCOMM.2022.3145291
10. BANERJEE, S., AGRAWAL, M. Underwater acoustic noise with generalized Gaussian statistics: Effects on error performance. In MTS/IEEE OCEANS - Bergen. Bergen (Norway), 2013, p. 1–8. DOI: 10.1109/OCEANS-Bergen.2013.6608191
11. SOURY, H., YILMAZ, F., ALOUINI, M.-S. Average bit error probability of binary coherent signaling over generalized fading channels subject to additive generalized Gaussian noise. IEEE Communications Letters, 2012, vol. 16, no. 6, p. 785–788. DOI: 10.1109/lcomm.2012.040912.112612
12. ZEDINI, E., OUBEI, H. M., KAMMOUN, A., et al. Unified statistical channel model for turbulence-induced fading in underwater wireless optical communication systems. IEEE Transactions on Communications, 2019, vol. 67, no. 4, p. 2893–2907. DOI: 10.1109/tcomm.2019.2891542
13. AMMAR, S., AMIN, O., ALOUINI, M.-S., et al. Energy-aware underwater optical system with combined solar cell and SPAD receiver. IEEE Communications Letters, 2022, vol. 26, no. 1, p. 59–63. DOI: 10.1109/lcomm.2021.3127198
14. BOLUDA-RUIZ, R., GARCIA-ZAMBRANA, A., CASTILLOVAZQUEZ, C., et al. Novel approximation of misalignment fading modeled by Beckmann distribution on free-space optical links. Optics Express, 2016, vol. 24, no. 3, p. 22635–22649. DOI: 10.1364/oe.24.022635
15. LI, S., YANG, L., DA COSTA, D. B., et al. Performance analysis of UAV-based mixed RF-UWOC transmission systems. IEEE Transactions on Communications, 2021, vol. 69, no. 8, p. 5559–5572. DOI: 10.1109/tcomm.2021.3076790
16. MATHAI, A. M., SAXENA, R. K., HAUBOLD, H. J. The HFunction: Theory and Applications. New York (USA): Springer, 2010. ISBN: 9781441909152
17. TENG, T. Outage probability and ergodic capacity analysis of satellite-terrestrial NOMA system with mixed RF/mmWave relaying. Journal of Physics Communications, 2023, vol. 57, no. C, p. 1–9. DOI: 10.1016/j.phycom.2023.101998
18. PRUDNIKOV, A. P., BRYCHKOV, Y. A., MARICHEV, O. I. Integrals and Series, Vol. 3: More Special Functions. New York (USA): Gordon and Breach Science Publishers, 1989. ISBN: 9782881246821
19. PRUDNIKOV, A. P., BRYCHKOV, Y. A., MARICHEV, O. I, et al. On the performance of cognitive satellite-terrestrial networks. IEEE Transactions on Cognitive Communications and Networking, 2017, vol. 3, no. 4, p. 668–683. DOI: 10.1109/tccn.2017.2763619
20. GRADSHTEYN, I. S., RYZHIK, I. M. Table of Integrals, Series, and Products. Academic Press, 2014. ISBN: 9780123849335
21. MILGRAM, M. S. The generalized integro-exponential function. Mathematics of Computation, 1985, vol. 44, no. 170, p. 443–458. DOI: 10.1090/s0025-5718-1985-0777276-4
22. KILBAS, A. A. H-Transforms: Theory and Applications. Boca Raton (FL, USA): CRC Press, 2004. ISBN: 9780429205163

Keywords: Dual-hop RF/UWOC transmission, decode-and-forward relay, performance analysis, satellite-marine communication network

H. L. Sun, Z. H. Liao, W. D. Shen [references] [full-text] [DOI: 10.13164/re.2023.0625] [Download Citations]
A Random Access Scheme for Aggregate Traffic Based on Deep Fusion of Supermartingale and Improved SSA

The network services present diversity as the continuous evolution of communication scenarios, which brings a great challenge to the efficient utilization of resources. The ALOHA access mechanism is considered as an effective solution to deal with multi services for its feature of shared bandwidth. However, the collision problem of ALOHA degrades the quality of service (QoS) seriously. The multi packet reception (MPR) technology could mitigate collision and improve network performance. Considering ALOHA mechanism with MPR capability, we propose a novel random access scheme for aggregate traffic based on deep fusion of supermartingale and improved sparrow search algorithm (SSA) to provide delay QoS guarantee. Firstly, we construct a complicated queuing model with heterogeneous arrivals and ALOHA-type service. Secondly, we derive the tighter delay-violation probability bound relying on supermartingale theory, and the optimization problem is constructed with the goal of minimizing the service rate and the constraint of supermartingale bound. Finally, we improve the SSA by combining Circle chaotic map, nonlinear inertia weight and Levy flight strategy, then the scheme is designed by applying the improved SSA and supermartingale constraint. Simulation results show that the proposed algorithm has faster convergence speed and the scheme is more bandwidth-saving.

1. VELICHKOVSKA, B., CHOLAKOSKA, A., ATANASOVSKI, V. Machine learning based classification of IoT traffic. Radioengineering, 2023, vol. 32, no. 2, p. 256–263. DOI: 10.13164/re.2023.0256
2. JLASSI, W., HADDAD, R., BOUALLEGUE, R., et al. Increase of the lifetime of wireless sensor network using clustering algorithm and optimal path selection method. Radioengineering, 2022, vol. 31, no. 3, p. 301–311. DOI: 10.13164/re.2022.0301
3. MALAK, D., HUANG, H., ANDREWS, J. G. Throughput maximization for delay-sensitive random access communication. IEEE Transactions on Wireless Communications, 2019, vol. 18, no. 1, p. 709–723. DOI: 10.1109/TWC.2018.2885295
4. CHEN, Z., FENG, Y., TIAN, Z., et al. Energy efficiency optimization for irregular repetition slotted ALOHA-based massive access. IEEE Wireless Communications Letters, 2022, vol. 11, no. 5, p. 982–986. DOI: 10.1109/LWC.2022.3151931
5. ZHAO, L., CHI, X., YANG, S. Optimal ALOHA-like random access with heterogeneous QoS guarantees for multi-packet reception aided visible light communications. IEEE Transactions on Wireless Communications, 2016, vol. 15, no. 11, p. 7872–7884. DOI: 10.1109/TWC.2016.2608956
6. CHANG, C. S., THOMAS, J. A. Effective bandwidth in high speed digital networks. IEEE Journal on Selected Areas in Communications, 1995, vol. 13, no. 6, p. 1091–1100. DOI: 10.1109/49.400664
7. BAVIO, J., MARRON, B. Properties of the estimators for the effective bandwidth in a generalized Markov fluid model. Open Journal of Statistics, 2018, vol. 8, no. 1, p. 69–84. DOI: 10.4236/ojs.2018.81006
8. ABRAHÃO, D. C., VIEIRA, F. H. T. Resource allocation algorithm for LTE networks using fuzzy based adaptive priority and effective bandwidth estimation. Wireless Networks, 2018, vol. 24, p. 423–437. DOI: 10.1007/s11276-016-1344-6
9. WU, D., NEGI, R. Effective capacity: A wireless link model for support of quality of service. IEEE Transactions on Wireless Communications, 2003, vol. 2, no. 4, p. 630–643. DOI: 10.1109/TWC.2003.814353
10. CHOI, J. Effective capacity of NOMA and a suboptimal power control policy with delay QoS. IEEE Transactions on Communications, 2017, vol. 65, no. 4, p. 1849–1858. DOI: 10.1109/TCOMM.2017.2661763
11. ZHAO, L. L., CHI, X. F., SHI, W. A QoS-driven random access algorithm for MPR-capable VLC system. IEEE Communications Letters, 2016, vol. 20, no. 6, p. 1239–1242. DOI: 10.1109/lcomm.2016.2553661
12. CHI, X., JING, Y., SUN, H., et al. A random compensation scheme for 5G slicing under statistical delay-QoS constraints. IEEE Access, 2020, vol. 8, p. 195197–195205. DOI: 10.1109/ACCESS.2020.3033321
13. POLOCZEK, F., CIUCU, F. A martingale-envelope and applications. ACM SIGMETRICS Performance Evaluation Review, 2013, vol. 41, no. 3, p. 43–45. DOI: 10.1145/2567529.2567543
14. POLOCZEK, F., CIUCU, F. Scheduling analysis with martingales. Performance Evaluation, 2014, vol. 79, p. 56–72. DOI: 10.1016/j.peva.2014.07.004
15. POLOCZEK, F., CIUCU, F. Service-martingales: Theory and applications to the delay analysis of random access protocols. In 2015 IEEE Conference on Computer Communications. Hong Kong (China), 2015, p. 945–953. DOI: 10.1109/infocom.2015.7218466
16. HU, Y., LI, H., CHANG, Z., et al. Scheduling strategy for multimedia heterogeneous high-speed train networks. IEEE Transactions on Vehicular Technology, 2016, vol. 66, no. 4, p. 3265–3279. DOI: 10.1109/tvt.2016.2587080
17. HU, Y., LI, H., CHANG, Z., et al. End-to-end backlog and delay bound analysis for multi-hop vehicular ad hoc networks. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 10, p. 6808–6821. DOI: 10.1109/TWC.2017.2731847
18. ZHAO, L., CHI, X., ZHU, Y. Martingales-based energy-efficient D-ALOHA algorithms for MTC networks with delay insensitive/URLLC terminals co-existence. IEEE Internet of Things Journal, 2018, vol. 5, no. 2, p. 1285–1298. DOI: 10.1109/JIOT.2018.2794614
19. LIU, T., SUN, L., CHEN, R., et al. Martingale theory-based optimal task allocation in heterogeneous vehicular networks. IEEE Access, 2019, vol. 7, p. 122354–122366. DOI: 10.1109/access.2019.2914942
20. LIU, S., CHI, X., ZHAO, L. Bandwidth allocation under multilevel service guarantees of downlink in the VLC-OFDM system. Journal of the Optical Society of Korea, 2016, vol. 20, no. 6, p. 704–715. DOI: 10.3807/JOSK.2016.20.6.704
21. DONG, X., CHI, X., SUN, H., et al. Scheduling with heterogeneous QoS provisioning for indoor visible-light communication. Current Optics and Photonics, 2018, vol. 2, no. 1, p. 39–46. DOI: 10.3807/COPP.2018.2.1.039
22. OZCAN, G., OZMEN, M., GURSOY, M. C. QoS-driven energy efficient power control with random arrivals and arbitrary input distributions. IEEE Transactions on Wireless Communications, 2016, vol. 16, no. 1, p. 376–388. DOI: 10.1109/TWC.2016.2623620
23. XUE, J., SHEN, B. A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science & Control Engineering, 2020, vol. 8, no. 1, p. 22–34. DOI: 10.1080/21642583.2019.1708830
24. LITTLE, J. D., GRAVES, S. C. Little's Law. In CHHAJED, D., LOWE, T. J. (eds.) Building Intuition: Insights from Basic Operations Management Models and Principles, 2008, p. 81–100. DOI: 10.1007/978-0-387-73699-0_5
25. ZHANG, D. M., XU, H., WANG, Y. R., et al. Whale optimization algorithm for embedded Circle mapping and one-dimensional oppositional learning based small hole imaging. Control and Decision, 2021, vol. 36, no. 5, p. 1173–1180. DOI: 10.13195/j.kzyjc.2019.1362
26. ZHANG, C. X., ZHOU, K. Q., YE, S. Q., et al. An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem. IEEE Access, 2021, vol. 9, p. 161352–161373. DOI: 10.1109/ACCESS.2021.3130640
27. LU, X. L., HE, G. QPSO algorithm based on Levy flight and its application in fuzzy portfolio. Applied Soft Computing Journal, 2021, vol. 99, p. 1–9. DOI: 10.1016/j.asoc.2020.106894
28. KATHIROLI, P., KANMANI, S. An efficient cluster-based routing using sparrow search algorithm for heterogeneous nodes in wireless sensor networks. In 2021 International Conference on Communication Information and Computing Technology (ICCICT). Mumbai (India), 2021, p. 1–6. DOI: 10.1109/ICCICT50803.2021.9510032
29. NASERI, A., JAFARI NAVIMIPOUR, N. A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 2019, vol. 10, p. 1851–1864. DOI: 10.1007/s12652-018-0773-8

Keywords: Supermartingale, improved sparrow search algorithm, quality of service, multi packet reception, aggregate traffic.

X. Zhao, B. Shi, J. Bai, F. Shu, Y. Chen, X. Zhan, W. Cai, M. Huang, Q. Jie, Y. Li, J. Wang, X. You [references] [full-text] [DOI: 10.13164/re.2023.0634] [Download Citations]
Machine-Learning-Aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Integrating the massive HAD-MIMO with direction of arrival (DOA) will provide an even ultra-high performance of DOA measurement, which can the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we consider three parts: detection, estimation, and Cramer-Rao lower bound (CRLB). First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to estimate the DOA and eliminate phase ambiguity using only one time block. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB.

1. ZEKAVAT R., BUEHRER R. M. Handbook of Position Location: Theory, Practice, and Advances. Piscataway (USA): Wiley-IEEE Press, 2019. ISBN: 9781119434580
2. HUANG, H., GUI, G., GACANIN, H., et al. Deep regularized waveform learning for beam prediction with limited samples in non-cooperative mmwave systems. IEEE Transactions on Vehicular Technology, 2023, vol. 72, no. 7, p. 9614–9619. DOI: 10.1109/TVT.2023.3248224
3. 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: 10.1007/978-3-031-41812-92
4. SHU, F., SHEN, T., XU, L., et al. Directional modulation: A physical-layer security solution to B5G and future wireless networks. IEEE Network, 2020, vol. 34, no. 2, p. 210–216. DOI: 10.1109/MNET.001.1900258
5. TUNCER, T. E., FRIEDLANDER, B., Classical and Modern Direction-of-Arrival Estimation. Burlington (USA): Academic Press, 2009. ISBN: 9780123745248
6. WANG, S., JACKSON, B. R., RAJAN, S., et al. Received signal strength-based emitter geolocation using an iterative maximum likelihood approach. In IEEE Military Communications Conference (MILCOM). San Diego (CA, USA), 2013, p. 68–72. DOI: 10.1109/MILCOM.2013.21
7. CHENG, X., SHU, F., LI, Y., et al. Optimal measurement of drone swarm in RSS-based passive localization with region constraints. IEEE Open Journal of Vehicular Technology, 2023, vol. 4, p. 1–11. DOI: 10.1109/OJVT.2022.3213866
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. 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
10. WEN, F., GUI, G., GACANIN, H., et al. Compressive sampling framework for 2D-DOA and polarization estimation in mmwave polarized massive mimo systems. IEEE Transactions on Wireless Communications, 2023, vol. 22, no. 5, p. 3071–3083. DOI: 10.1109/TWC.2022.3215965
11. GUI, G., LIU, M., TANG, F., et al. 6G: Opening new horizons for integration of comfort, security, and intelligence. IEEE Wireless Communications, 2020, vol. 27, no. 5, p. 126–132. DOI: 10.1109/MWC.001.1900516
12. SHI, B., JIANG, X., CHEN, N., et al. Fast ambiguous DOA elimination method of DOA measurement for hybrid massive MIMO receiver. Science China Information Sciences, 2022, vol. 65, no. 5, p. 1–2. DOI: 10.1007/s11432-021-3314-4
13. ZHANG, X., LAI, X., ZHENG, W., et al. Sparse array design for DOA estimation of non-circular signals: Reduced co-array redundancy and increased DOF. IEEE Sensors Journal, 2021, vol. 21, no. 24, p. 27928–27937. DOI: 10.1109/JSEN.2021.3122430
14. MENG, X., ZHU, J. A generalized sparse Bayesian learning algorithm for 1-bit DOA estimation. IEEE Communications Letters, 2018, vol. 22, no. 7, p. 1414–1417. DOI: 10.1109/LCOMM.2018.2834904
15. SHI, B., CHEN, N., ZHU, X., et al. Impact of low-resolution ADC on DOA estimation performance for massive MIMO receive array. IEEE Systems Journal, 2022, vol. 16, no. 2, p. 2635–2638. DOI: 10.1109/JSYST.2021.3139449
16. HUANG, H., YANG, J., HUANG, H., et al. Deep learning for superresolution 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
17. ZHUANG, Z., XU, L., LI, J., et al. Machine-learning-based high-resolution DOA measurement and robust directional modulation for hybrid analog-digital massive MIMO transceiver. Science China Information Sciences, 2020, vol. 63, no. 8, p. 1–18. DOI: 10.1007/s11432-019-2921-x
18. SHI, B., JIE, Q., SHU, F., et al. DOA estimation using massive receive MIMO: Basic principles, key techniques, performance analysis, and applications. arXiv, 2021, p. 1–12. DOI: 10.48550/arXiv.2109.00154
19. LI, Y., ZHANG, Z., WU, L., et al. 5G communication signal based localization with a single base station. In IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). Victoria (BC, Canada), 2020, p. 1–5. DOI: 10.1109/VTC2020-Fall49728.2020.9348766
20. WANG, Y., HO, K. C. Unified near-field and far-field localization for AOA and hybrid AOA-TDOA positioning. IEEE Transactions on Wireless Communications, 2018, vol. 17, no. 2, p. 1242–1254. DOI: 10.1109/TWC.2017.2777457
21. JIE, Q., ZHAN, X., SHU, F., et al. High-performance passive eigenmodel-based detectors of single emitter using massive MIMO receivers. IEEE Wireless Communications Letters, 2022, vol. 11, no. 4, p. 836–840. DOI: 10.1109/LWC.2022.3146829
22. ZHANG, R., LIM, T. J. , LIANG, Y.-C., et al. Multi-antenna based spectrum sensing for cognitive radios: A GLRT approach. IEEE Transactions on Communications, 2010, vol. 58, no. 1, p. 84–88. DOI: 10.1109/TCOMM.2010.01.080158

Keywords: DOA, hybrid analog and digital, MIMO, green technologies, CRLB, multi-layer-neural-network