A novel balanced nonreciprocal bandpass filter based on the stepped impedance resonator and the time-modulated resonator is proposed in this paper. The balanced nonreciprocal bandpass filter is fabricated on a single PCB board, and the compact structure is achieved through the gap coupling structure of the microstrip resonators. Utilizing the quarter-wavelength transformer and gap coupling structure, good isolation between RF and modulated signals is achieved without adding lumped elements. Relying on the efficient modulation circuit and the half-wavelength stepped impedance resonator’s inherent resonance characteristic, an excellent nonreciprocal characteristic of the differential signal and effective suppression of common-mode noise are achieved. A balanced microstrip nonreciprocal bandpass filter operating at the center frequency of 1.5 GHz is designed, simulated, and experimentally verified. The measured reverse isolation is greater than 20 dB with a bandwidth of 48.8 MHz. The measured forward differential-mode insertion loss is 3.7 dB at the center frequency of 1.5 GHz. In the range of 1.2–1.8 GHz, the measured common-mode noise suppression is larger than 60.4 dB.
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A. M. A. Mirza, A. Khawaja, S. Mughal, R. A. Butt[references][full-text][DOI: 10.13164/re.2024.0502][Download Citations] Distributed Symmetric Turbo Coded OFDM Scheme Incorporated with STBC-MIMO Antennas for Coded-Cooperative Wireless Communication under Wideband Noise Jamming Environment
This research paper proposes a novel anti-jamming technique based on a single-relay distributed symmetric Turbo coded orthogonal frequency division multiplexing (DSTC-OFDM) scheme. The stated scheme is incorporated with Alamouti space-time block code (STBC) multiple-input multiple-output (MIMO) 2×2 antennas for coded-cooperative wireless communication system under wideband noise jamming environment. As a suitable benchmark for comparison, a conventional symmetric Turbo coded OFDM (STC-OFDM) scheme incorporated with Alamouti STBC-MIMO (2×2) antennas is also developed for non-cooperative wireless communication system under the same jamming environment. Moreover, both the proposed MIMO schemes are compared with the corresponding single-antenna schemes. In this research, the modulation techniques employed are binary phase-shift keying and M-ary quadrature amplitude modulation while soft-demodulators are used at the destination node along with a joint iterative soft-input/soft-output decoding tech-nique. According to the Monte Carlo simulation results, the proposed DSTC-OFDM-MIMO (coded-cooperative) scheme with Alamouti-STBC (2×2) antennas outperforms the STC-OFDM-MIMO (non-cooperative) scheme by a gain that ranges between 0.5–6 dB for different jamming scenarios in the high SNR simulated region under the same conditions, i.e., the code rates Rc = 1/3 and data frame lengths l = 512 bits for both the proposed schemes. However, in the low SNR simulated region, the STC-OFDM-MIMO scheme shows similar performance as the DSTC-OFDM-MIMO scheme, under identical conditions. Furthermore, the proposed distributed scheme with STBC-MIMO (2×2) antennas incorporates both coding gain and cooperative diversity gain.
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A multi-channel differential synchronous demodulator is proposed for third harmonics suppression in three-phase receiving coils based linear inductive position sensor, which is realized by taking envelope differences of every two induced signals on three-phase receiving coils as differential signals. The proposed demodulator includes a differential synchronous envelope detector (DSED), and a time division MUX (TDM). By using the DSED, the envelope differences are synchronously demodulated as differential signals. The TDM including a multi-channel selector, a MUX and a voltage follower, is used for independently outputting the corresponding differential signals for linear position calculation. The proposed demodulator was designed in a 180 nm CMOS process and verified by Spectre simulator. Simulation results show third harmonics of differential signals are improved by 17.4 dB, indicating the proposed demodulator could be a candidate for designing the high performance analog front-end of inductive position sensor.
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Keywords: Inductive position sensor, multi-channel, differential synchronous demodulator, third harmonics noise
The combination of reconfigurable intelligent surface (RIS) with the unmanned aerial vehicle (UAV) and the non-orthogonal multiple access (NOMA) has emerged as a promising technology in the sixth generation (6G) network. However, UAV downlinks are susceptible to potential eavesdropping attacks due to the open and broadcasting nature of wireless channels. In addition, transmitting information with infinite blocklength (IBL) is impractical in 6G applications. In this paper, we propose a secure RIS-assisted UAV communication system based on NOMA with finite blocklength (FBL) transmission. To maximize the average secrecy rate for all ground users under the mobility and power constraints of the UAV, we jointly optimize the phase shift of the RIS, the trajectory, the transmit power of the UAV, and the user scheduling. To solve the formulated non-convex problem, we first transform the optimization problem into four convex sub-problems, i.e., phase shift optimization, trajectory optimization, transmit power optimization, and user scheduling optimization. Then, an iterative algorithm is developed based on the successive convex approximation (SCA) to solve the four sub-problems. Numerical results show that the average secrecy rate for all ground users achieved with the proposed algorithm is higher than that achieved with the traditional algorithms.
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This paper presents a study of six physical transmission line transformers (TLTs) designed to provide wideband output matching for laterally diffused metal oxide semiconductor (LDMOS) transistors within a push-pull amplifier operating in the 1.8-30 MHz spectrum with an output power of 600 W. While the mathematical model of TLTs is well described in the literature, the impact of physical construction methods on impedance matching and real amplifier performance is more challenging to ascertain. This paper compares six different TLTs built on various ferrite cores and employing different implementations of transmission lines. Return loss below -14 dB was achieved from 1.65 to 37.4 MHz, with most of the tested transformers exhibiting return loss better than -10 dB up to 50 MHz. The study also presents the impact of transmission line implementation on impedance matching using both special-purpose low impedance coaxial cable and a combination of general-purpose coaxial cables connected in parallel. Comparison of three chosen transformers in a real RF amplifier shows that using parallel transmission lines can lead to a return loss comparable to that of a special-purpose coaxial cable, although at the cost of lower efficiency and output power. Second harmonic cancellation effect was also investigated for three transformers.
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Keywords: Normal distribution, Walsh code, bit error rate, covert communication, IoT
To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity. To address this problem, a multi-level tree-based DNN model (TDNN) is proposed as an alternative , where each level takes small-scale multi-layer neural networks (MLNNs) as nodes to divide the target angular interval into multiple sub-intervals, and each output class is associated to a MLNN at the next level. Then the number of MLNNs is gradually increasing from the first level to the last level, and so increasing the depth of tree will dramatically raise the number of output classes to improve the estimation accuracy. More importantly, this network is extended to make a multi-emitter DOA estimation. Simulation results show that the proposed TDNN performs much better than conventional DNN and root multiple signal classification algorithm (root-MUSIC) at extremely low signal-to-noise ratio (SNR) with massive multiple input multiple output (MIMO) receive array, and can achieve Cramer-Rao lower bound (CRLB). Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made a substantial performance enhancement over DNN and Root-MUSIC, and this gain grows as the number of emitters increases.
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Keywords: DOA estimation, DNN, massive MIMO, multi-label learning
In this paper, a system utilizing an active intelligent reflecting surface (IRS) to enhance the performance of wireless communication network is modeled, which has the ability to adjust power between base station (BS) and active IRS. We aim to maximize the signal-to-noise ratio (SNR) of the user by jointly designing power allocation (PA) factor, active IRS phase shift matrix, and beamforming vector of BS, subject to a total power constraint. To tackle this non-convex problem, we solve this problem by alternately optimizing these variables. The PA factor is designed via polynomial regression method in machine learning. BS beamforming vector and IRS phase shift matrix are obtained by Dinkelbach's transform and successive convex approximation methods. Then, we maximize achievable rate (AR) and use closed-form fractional programming (CFFP) method to transform the original problem into an equivalent form. This problem is addressed by iteratively optimizing auxiliary variables, BS and IRS beamformings. Thus, two iterative PA methods are proposed accordingly, namely maximizing SNR based on PA factor (Max-SNR-PA) and maximizing AR based on CFFP (Max-AR-CFFP). The former has a better rate performance, while the latter has a lower computational complexity. Simulation results show that the proposed algorithms can effectively improve the rate performance compared to fixed PA strategies, only optimizing PA factor, aided by passive IRS, and without IRS.
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Keywords: Active intelligent reflecting surface, achievable rate, power allocation, closed-form fractional programming
End-to-End Encryption (E2EE) plays an essential role in safeguarding user privacy and protecting sensitive data across various communication platforms, including messaging applications, email services, and Internet of Things (IoT) devices. This paper presents a Hybrid Cryptography-Based E2EE method implemented on a Software Defined Networking (SDN) infrastructure, to strengthen bidirectional data security between hosts and IoT devices via the non-secure Message Queuing Telemetry Transport (MQTT) port. By addressing the threat of Man-in-the-Middle (MitM) attacks, the proposed system ensures that only authorized users can decrypt transmitted messages. This paper thoroughly analyzes the implementation and advantages of our Hybrid Cryptography-Based E2EE method by comparing RSA and ECC encryption techniques. ECC-256 is favored for key generation, owing to its high efficiency and speed, measured at 0.4009 ms. Additionally, through a comparison of RSA, AES, and ChaCha20 algorithms, AES-256 emerges as the optimal encryption choice, demonstrating the fastest encryption and decryption times for publishing 0.2758 ms and 0.1781 ms, respectively and for subscribing, with encryption at 0.2542 ms and decryption at 0.1577 ms. Along with its minimal packet size and low resource consumption, our proposed Hybrid Cryptography-Based E2EE method, implemented on SDN infrastructure, validate it's effectiveness in securing digital communications within SDN environments compared to existing solutions.
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Keywords: Software-Defined Networking (SDN), cyber security, Man-in-the-Middle (MitM), end-to-end encryption, Internet of Things (IoT)
In modern radar systems, the use of generalized compound distributed models can more accurately describe the amplitude distribution characteristics of sea clutter, which is crucial for radar signal processing and sea target detection. However, traditional zero memory nonlinearity (ZMNL) method cannot simulate generalized compound distributed sea clutter with arbitrary shape parameters. To address this issue, an improved method for generating random variable was proposed, which combines the characteristics of the Gamma distribution and uses the additivity of its shape parameter. By increasing the branches for generating the Gamma distributed random variables, the Probability Density Function (PDF) of the Gamma function is transformed into a second-order nonlinear ordinary differential equation, and the Gamma distributed random variables with arbitrary shape parameters are solved. Finally, the Generalized Gamma (GГ) distributed random variables under arbitrary shape parameter can be obtained through specific nonlinear transformations. This method extends the shape parameters of generalized compound distributed clutter to general real numbers. Through comparative experiments with measured data, the generalized compound distributed model has strong universality and can more accurately represent measured data. Finally, the results of clutter simulation experiments also indicate that the proposed method is not only suitable for clutter simulation with non-integer or non-semi- integer shape parameters, but also further improves the fitting degree.
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Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, this paper proposes a high resolution imaging algorithm for ISAR based on weighted adaptive mixed norm. By weighting against l_2,0 mixed norm term, an improved model of the sparse constraint ISAR signal is proposed. The model effectively distinguishes the signal and noise by adding the weight coefficient, and improves the reconstruction accuracy of the strong scattering center. Meanwhile, the weight coefficients in this improved model can be iteratively updated in each cycle to improve the image reconstruction accuracy. The optimization model takes advantage of mixed norm to achieve fast convergence in the operation, and adopts conjugate gradient descent method and fast Fourier transform operation in the solution, which simplifies the solving process of the optimization problem and improves the operation efficiency of the algorithm. Simulation data and measured data verify the effectiveness of the proposed method.
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To reduce the circuit size of a single-ended-to-balanced power divider, a section of coupled line with a short-circuited terminal is applied. In this structure, two transmission zeros are generated, which realizes a wide input-matching bandwidth without extra absorbing branches. The enhanced input matching bandwidth can decrease the effect of the reflection wave power on the operation of the preceding active stage in the RF front end. Equations are derived to support the power transmission in a specific power division ratio. To verify the proposed structure, a single-ended-to-balanced power divider operating at 2.0 GHz is designed, fabricated, and measured. A wide matching bandwidth under −10 dB covers from 0.292 to 3.966 GHz with a fractional bandwidth (FBW) of 183.7%. Simulation and measurement results are in good agreement.
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Keywords: SETB power divider, enhanced input-matching bandwidth, out-of-phase, coupled line
In this paper, our focus is towards resource allocation in a multiuser downlink non-orthogonal multiple access (NOMA) system. In the power-domain NOMA, where multiple access is realized by assigning different power levels to the clustered users, a certain degree of advantage of NOMA depends on clustering of users and power levels allocated to them. This study proposes a new power allocation algorithm, based on sum rate as performance criterion, which is applied for the clusters defined by High-High/High-Low pairing scheme. The proposed algorithm takes into account fairness between clustered users from the acquired users’ rate point of view. It provides better sum rate performance of NOMA compared to OMA, but also a low gap between the individual rates of paired users. The detailed numerical and independent simulation results for the downlink NOMA over general alpha-F fading channels are shown.
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Keywords: Non-orthogonal Multiple Access (NOMA), composite alpha-F fading, user pairing, resource power allocation, downlink NOMA, rate fairness
This paper considers a novel design technique of compact dual-BPF and four-channel diplexer for a multi-service communication system. The suggested dual-band passband filter is constructed by a double-layered stub loaded-stepped impedance resonator (SL-SIR), leading to a tiny circuit area, and lightweight, low cost, and good characteristic performance. Herein, the SL-SIR resonant odd-mode is used to realize the first passband, while the resonant even-mode is used to realize the second passband. Also, the proposed three-port quad-channel diplexer is performed by two different double-layered dual-passband filters, which also have a very compact circuit area. For practical verification, a two-passband filter working at 2.5/4 GHz with a circuit area of 91.7〖mm〗^2 and a four-channel diplexer working at 2.5/4 GHz and 3.5/5.2 GHz with a circuit area of 0.0639 〖λ_g〗^2 excluding feeding ports are designed, manufactured, and measured. The electromagnetic simulated and measured responses are compared and discussed. Obviously, the comparison show good agreement improving the expected filtering response. The diplexer offers insertion/return losses of about (0.44/0.74) dB/(0.45/1.12) dB for channel 1/channel 2, and (21.30/21.72) dB/(19.81/20.54) dB for channel 3/channel 4.
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A dual-band Half Mode Substrate Integrated Waveguide (HMSIW) slot antenna operating at 3.5 GHz and 4.9 GHz is proposed. The two operational bands of the proposed antenna are achieved by combining two HMSIW slot antennas. The -10 dB impedance bandwidth and realized gain of the antenna at 3.5 GHz and 4.9 GHz are 3.47-3.68 GHz with a gain of 3.9 dBi and 4.62-5.16 GHz with a gain of 5.5 dBi, respectively. Compared with the traditional SIW dual-band antenna, the bandwidth of the antenna has been significantly improved. The proposed antenna has a simple structure and exhibits excellent radiation performance in both operating frequency bands, making it suitable for 5G mobile communication systems.
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Deep learning-based methods have recently made enormous progress in remote sensing image compression. However, conventional CNN is complex to adaptively capture important information from different image regions. In addition, previous transformer-based compression methods have introduced high computational complexity to the models. Remote sensing images contain rich spatial and channel information. The effective extraction of these two kinds of information for image compression remains challenging. To address these issues, we propose a new low-complexity end-to-end image compression framework combining CNN and transformer. This framework includes two critical modules: the Dynamic Attention Model (DAM) and the Hyper-Prior Hybrid Attention Model (HPHAM). By employing dynamic convolution as the core part of the DAM, the DAM can dynamically adjust the attention weights according to the image content. HPHAM effectively integrates non-local and channel information of latent representations through the parallel running of Gated Channel Attention (GCA) and multi-head self-attention. Experiments demonstrate that the proposed approach outperforms existing mainstream deep-learning image compression approaches and conventional image compression methods, achieving optimal rate-distortion performance on three datasets. Code is available at https://github.com/jiahuiLiu11/LTCHM.
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Energy storage systems are increasingly playing a pivotal role in power systems. The combination of distributed sources and energy storage systems with local loads forms an autonomous distribution grid (ADG) capable of flexibly operating in either islanding or grid-connected modes. It is essential to maintain the state variables of the grid, such as frequency and voltage, within permitted ranges during islanding mode, where the power converter control plays the most important role in the system operation. This paper proposes a control method for the power converter associated with a hybrid energy storage system (ESS) that is capable of forming the distribution grid during the islanding mode as well as following to the grid in grid-connected mode. The control method is based on a centralized energy controller to distribute energy between a battery and a supercapacitor. Accordingly, two voltage and current control loops using a PI hybrid fuzzy controller are designed. Simulation is developed using Matlab/Simulink software for a case study of a distribution network to illustrate the efficiency of the proposed control method.
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Keywords: Converters, energy storage systems, power distribution networks, supercapacitors
Today, the anywhere, anyhow and anytime application scenarios of 5G system force designer to challenge on electromagnetic interference (EMI) requirements. Despite the technological progress, relevant test techniques are necessary to minimize the future communication system EMI risk. In this paper, the EMI characterization from nonlinearity (NLT) of 5G system Gallium Nitride (GaN) power amplifier (PA) is studied. Firstly, the PA NLT is evaluated by 1-dB/3-dB/6-dB compression point and 3rd-order intermodulation distortion (IMD3). Then, a measurement platform is built based on vector signal generator and EMI receiver including digital modulation system. According to the adjacent channel leakage ratio (ACLR), error vector magnitude (EVM) and signal-to-noise ratio (SNR), the EMI characteristics of 3.5-GHz carrier signals modulated by 16-Quadrature Amplitude Modulation (16-QAM) distorted by the GaN PA NLT are discussed. Due to the GaN PA 3rd order intermodulation (IM3) product, the SNR degrades from 34.8 dB to 14.6 dB when the input signal power increases from -10 dBm to 6 dBm. The EMI effect is confirmed by significant signal distortion observed with 16-QAM constellation diagram. Research work is currently ongoing for extending the EMI test technique for 6G communication system.
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Keywords: EMI impact, nonlinear characterization, 5G communication, 16-QAM, signal distortion, signal-to-noise ratio (SNR)
Active jamming's flexibility and variability pose significant challenges for frequency-agility radar (FAR) detection, as it can continuously intercept and retransmit radar signals to suppress or deceive the radar. To tackle this, we propose an intelligent learning method for FAR based on reinforcement learning (RL), integrating signal processing with compressed sensing (CS). We introduce an inter-pulse carrier-frequency hopping combined with intra-pulse sub-frequency coding (IPCFH-IPSFC) signal model to address time-domain discontinuities caused by active jamming, enabling effective mutual masking of pulses through agile waveform parameters. We develop jamming signal models and design four jamming strategies based on two common types of active jamming, providing essential data for the FAR intelligent learning method. To enhance FAR’s adaptive anti-jamming and target detection performance, we propose an RL-based intelligent learning model. This model includes five submodules: signal processing, anti-jamming evaluation, target detection, optimization constraint design, and optimization algorithm design. We apply a proximal policy optimization combined with a generative pre-trained transformer (PPO-GPT) to solve this model, allowing FAR to adaptively learn jamming strategies and optimize IPCFH-IPSFC waveform parameters for effective anti-jamming. Simulation results confirm that our method achieves robust performance and rapid convergence, finding optimal anti-jamming strategies in just 215 training iterations. The FAR effectively counteracts jamming while accurately estimating target range and velocity.
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The current data sharing schemes mainly employ Attribute-Based Encryption (ABE) technique to achieve one-to-many access control for power data. However, these schemes suffer from issues such as low encryption efficiency and vulnerability to user attribute tampering. To address these problems, a power data sharing scheme based on multi-strategy access trees is proposed. By combining ABE with symmetric encryption algorithms, specifically employing the Advanced Encryption Standard (AES) in conjunction with Ciphertext-Policy ABE (CP-ABE), a hybrid encryption mechanism is adopted. Building upon an encryption algorithm rooted in multi-strategy access trees, data visitors are categorized into security levels according to roles and regions. Then, a time-constrained attribute encryption scheme is proposed for designated personnel, thereby achieving confidentiality and fine-grained access control for power data. Analysis results indicate that the proposed scheme enables secure sharing of power data and is highly suitable for resource-limited power terminal devices.
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Automatic Modulation Classification (AMC) has emerged as a critical research domain with wide-ranging applications in both civilian and military contexts. With the advent of artificial intelligence, deep learning techniques have gained prominence in AMC due to their unparalleled ability to automatically extract relevant features. However, most contemporary AMC models rely heavily on downsampling strategies to increase the receptive field while reducing computational complexity. Empirical evidence indicates that progressive downsampling substantially reduces the spatial resolution of feature maps, leading to poor generalization, particularly for closely related modulation schemes. To address these challenges, this paper proposes a novel Multiscale Dilated Pyramid Module (MDPM). In contrast to traditional downsampling techniques, MDPM mitigates resolution loss and retains a broader range of features, facilitating more comprehensive recognition. Furthermore, the multiscale features captured by MDPM enhance the robustness of the model to noise, thereby improving classification performance in noisy environments. The model's efficiency is further optimized through the integration of group convolutions and channel shuffle techniques. Extensive experimental results and evaluations confirm that the MDPM-based approach surpasses state-of-the-art methods, underscoring its significant potential for practical deployment. The signal data¬base and model can be freely accessed at https://pan.baidu.com/s/1g_HQXcRXshrT8nwKUNDYrQ?pwd=9ug6.
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Keywords: Automatic modulation classification, deep learning, spatial resolution, multi-scale dilated pyramid module, group convolution
Aiming at the challenge of infrared small target detection with different shape and size under the different scene, a novel algorithm architecture is proposed using the kernel of Target Detection Unit (TDU). The TDU incorporates the fractal geometry design and dual-scale structure, which can execute three main sub-tasks: preliminary target detection, target localization with high precision and target segmentation by pixel-level. First, the principle establishes a dual-scale target detection structure, selects the central point, decomposes the scale information and constructs the Integrated Local Contrast Saliency (ILCS) map, the target preliminary result is obtained by the visual attention mechanism of “top to bottom”. Second, the principle adopts the scale-recursion algorithm by the mechanism of “bottom to up” to locate the target precisely from the preliminary result along with Area Optimal Recommend Mechanism (AORM) strategy. At last, the separated local histogram is used to segment the target by per-pixel with suitable threshold. From the experimental results, conducted across five different types of infrared-scenes including infrared sky scene, infrared maritime scene, backlight illuminance scene, infrared scene with interference and infrared scene with small & dim target, we observe the performance of high accuracy rate and remarkable robustness.
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Keywords: Infrared target detection, target detection unit, target localization with high-precision, target segmentation by per-pixel, area optimal recommend mechanism
In this paper, a compact stepped slot antenna, for Global Navigation Satellite System (GNSS) applications, is proposed. The latter one, which operates in the frequency range from 1.49 GHz to 1.66 GHz, is designed to cover five GNSS bands namely GPS-L1, Galileo-E1, GLONASS-G1, BEIDOU-B1 and EGNOS-L1. The proposed composite design is carried out in three phases. Firstly, a compact slot antenna, having a size of 58.0times52.0times1.6 mm, is designed on FR4 substrate to generate the five GNSS bands. Secondly, a compact Frequency Selective Surface (FSS) reflector, with 18.0times18.0times1.6 mm unit size, is designed to produce a stop band response matching all these bands. Finally, a single layer FSS, consisting of 9times9 units, is combined with the optimized antenna to achieve a high-gain directional antenna structure. The final proposed combination has been investigated using the High Frequency Structure Simulator (HFSS) and validated by Computer Simulation Technology (CST) Microwave Studio. Besides, a prototype antenna is fabricated and validated by measurements. The measurement results, which illustrate a good agreement with those corresponding to the simulations, have shown that the proposed composite antenna exhibits a directional radiation pattern with a high peak gain of 7.3 dBi and a maximum gain improvement of 5.65 dBi at 1.57 GHz. Furthermore, it offers good radiation efficiency in the operating bands, which makes it a good candidate for multi-systems GNSS receivers, especially to reduce the different interferences by enhancing the antenna radiation characteristics.
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Keywords: FSS, GNSS, GPS, slot antenna, high gain antenna, multiband
To overcome the high peak-to-average-power ratio (PAPR) in orthogonal time sequency multiplexing (OTSM), this document proposes orthogonal and random selective mapping phase vectors (OSLM and RSLM) after some modifications to be applicable with OTSM constructions at both transmitter and receiver. Consequently, the PAPR (without explicit side information) of the original phase vectors is reduced and restored in the presence of a nonlinear power amplifier, and three different multipath fading channel models: Extended pedestrian A (EPA), Extended vehicular A (EVA) and Extended typical urban (ETU), with different user speed, namely 150 and 500 km/h. Also, complementary cumulative distribution function (CCDF) PAPR comparisons are provided based on the Walsh Hadamard transform (WHT) matrix W_N and the inverse symplectic fast Fourier transform (ISFFT) matrix F_N. Furthermore, three detectors are modified and implemented in this work, namely single tap MMSE (ST-MMSE), Gauss-Seidel iterative matched filter (MFGS) and linear MMSE (LMMSE). As a result, this proposal shows reliable performance in terms of PAPR reduction, bit error rate (BER) and side information error rate (SIER) of the OTSM system at a fraction of extension values (f=0.09 and C=1.15) while maintaining power efficiency.
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Ternary Content-Addressable Memory (TCAM) enables high-speed searches by comparing search data with all stored data in a single clock cycle, using ternary logic ("0", "1", "X" for "don't care") for flexible matching. This makes TCAM ideal for applications like network routers and lookup tables. However, TCAM's speed increases silicon area and limits memory capacity. This paper introduces a low-area, enhanced-capacity TCAM for IPv6 lookup tables using Don't Care Reduction (DCR) and Data Relocation (DR) techniques. The DCR technique requires only (N + log_2(N))-bit memory for an N-bit IP address, reducing the need for 2N-bit memory. The DR technique improves TCAM storage capabilities by classifying the IPv6 into 4 different prefix length types and relocating the data in the prefix bit into the "X" cells. The design features a 256x128-bit TCAM (eight 32x128-bit memory banks) on a 65 nm process with a 1.2 V operation voltage. Results show a 71.47% increase in area efficiency per stored IP value compared to conventional TCAM and a 20.97% increase compared to data-relocation TCAM.
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Keywords: Ternary content-addressable memory (TCAM), high-speed searches, IP version 6 (IPv6), IPv6 lookup table, don't care reduction (DCR), data relocation (DR), conventional TCAM (CV-TCAM), data-relocation TCAM (DR-TCAM), low-area technique, enhanced-capacity technique
