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Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems

IEEE TRANSACTIONS ON signal processing 2025年 73卷 1094-1109页

作者： Sun, Gangle Cao, Mengyao Wang, Wenjin Xu, Wei Studer, Christoph Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.

关键词： signal processing algorithms Channel estimation Wireless communication Approximation algorithms Vectors Optimization Machine learning algorithms Training Sparse matrices Decoding Active user detection cell-free communication channel estimation data detection deep unfolding grant-free transmission massive machine-type communication

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Super-Resolution Range-Velocity Estimate of Multiple Targets for OFDM-Based 5G Radar Based on Unitary Parallel Factor Method

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4048-4063页

作者： Cao, Chenghu Huang, Haisheng Zhao, Yongbo Xian Univ Posts & Telecommun Xian 710121 Peoples R China Yanan Univ Xian Innovat Coll Xian 710100 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Informat Sensing & Understanding Xian 710071 Peoples R China

In this paper, we consider the problem of joint range-velocity estimate of multiple targets in orthogonal frequency division multiplex (OFDM) transmit waveform-based 5G radar. A unitary parallel factor (PARAFAC) algorithm is proposed to achieve super-resolution estimate and outstanding performance, using forward-backward averaging scheme. The forward-backward averaging scheme is adopted to construct real-valued tensor signal model instead of the complex-valued one, yielding the better accuracy at modest complexity. The proposed unitary PARAFAC algorithm is performed by decomposing the real-valued tensor without signal subspace estimate. Due to the inherent smoothing processing of the proposed unitary PARAFAC algorithm, it can effectively deal with high correlated target. Additionally, the proposed unitary PARAFAC algorithm can automatically obtain pair parameters including range and velocity of the same target without additional pair-matching operation. More importantly the regularized alternative least squares (RALS) algorithm is used to improve the decomposition performance of the real-valued tensor while maintaining iteration stability. The numerical results are presented to demonstrate the superior performance, especially for high correlated and closely spaced targets in low-SNR scenario.

关键词： Radar signal processing algorithms Tensors 5G mobile communication OFDM Sensors Symbols Accuracy Radar applications Radar signal processing Forward-backward averaging scheme OFDM-based 5G radar real-valued tensor signal model regularized alterative least squares unitary parallel factor

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Hybrid DoATDoA Method for Impact Localization on Thin-Walled Structures Using Sensor Clusters

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IEEE SENSORS JOURNAL 2025年第4期25卷 6659-6672页

作者： Zeng, Xu Deng, Deshuang Yang, Hongjuan Yang, Zhengyan Yang, Lei Wu, Zhanjun Dalian Univ Technol Sch Mech & Aerosp Engn Dalian 116024 Peoples R China Jiangnan Univ Sch Fiber Engn & Equipment Technol Wuxi 214122 Peoples R China Dalian Univ Technol Sch Mat Sci & Engn Dalian 116024 Peoples R China

Impact monitoring technology plays a critical role in ensuring the structural integrity and safety of thin-walled engineering structures in service. This article presents a novel hybrid direction of arrival (DoA)-time difference of arrival (TDoA) method for impact localization on thin-walled engineering structures using sensor clusters. The methodology involves placing two sensor clusters on the structure to capture impact signals. Subsequently, narrowband Lamb wave signals at a specific frequency are extracted from impact signals using continuous wavelet transform (CWT). The normalized variance sequence (NVS) approach is then used to determine the TDoA, and phase differences are calculated to estimate the DoA. The DoA-based spatial beamforming focusing (SBF) technique and TDoA-based hyperbolic locus imaging algorithm are used for impact imaging. An imaging fusion step is introduced to combine the results of the two imaging techniques, accurately determining the impact location. Experimental validation of the proposed method is conducted through impact tests on three distinct structures: a large-scale plate, a complex riveted stiffened plate, and a 3-D thin-walled cylindrical structure. A comparative analysis with two existing methods demonstrates the superior imaging resolution and localization accuracy of the proposed approach, which remains effective even in the presence of measurement noise. In addition, the effects of sensor type, shape, and configuration on the localization results are discussed. This research contributes to the advancement of impact localization technology for thin-walled structures, with potential applications in structural health monitoring and safety assessment.

关键词： Sensors Location awareness Sensor arrays Imaging Array signal processing Direction-of-arrival estimation signal processing algorithms Monitoring Narrowband Estimation Direction of arrival (DoA) impact localization sensor cluster time difference of arrival (TDoA)

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A Power-and-Area-Efficient Channel-Interleaved Neural signal Processor for Wireless Brain-Computer Interfaces With Unsupervised Spike Sorting

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2025年第1期19卷 108-119页

作者： Hu, Zichen Zhou, Zhining Lyu, Hongming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China ShanghaiTech Univ State Key Lab Adv Med Mat & Devices Shanghai 201210 Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom AI Shanghai 201210 Peoples R China

Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neuroscience research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-mu W power consumption and occupies 0.0057 mm(2) for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.

关键词： Feature extraction signal processing algorithms Wireless communication Clustering algorithms Bandwidth Noise level Hardware Neural signal processing spike detection feature extraction unsupervised clustering spike sorting ASIC channel-interleaving CMOS low-power hardware-efficiency

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Enhanced Knowledge Tracing With Learnable Filter

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第1期12卷 198-209页

作者： Qian, Fulan Hu, Yetong Li, Guangyao Chen, Jie Wang, Shijin Zhao, Shu Anhui Univ Artificial Intelligence Inst Hefei 230601 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China IFLYTEK AI Res Hefei 230088 Peoples R China

The primary objective of knowledge tracing (KT) is to evaluate students' understanding and mastery of knowledge through their responses to exercises, which aids in predicting their future performance. Deep neural networks have been widely applied in the area of knowledge tracing and have demonstrated encouraging results. Nevertheless, in real-world scenarios, there is a substantial amount of noise in students' response records. These noises may amplify the inherent risk of overfitting in deep neural networks, leading to a decrease in model performance. To address these issues, we introduce a new model called filter knowledge tracing (FKT). This innovative model incorporates a learnable filter into KT to filter out noise information from students' exercise sequences. We redefine the input paradigm of the data, using learnable filters to perform filtering operations in its frequency domain representation space, effectively removing noise. Additionally, an attention module has been introduced in the FKT model to evaluate the impact of students' historical interactions on their current knowledge state. To validate our model, we conduct extensive experiments utilizing four publicly available datasets. The results demonstrate that FKT outperforms existing benchmarks, particularly on larger datasets, signifying an improvement in KT performance while effectively reducing the risk of overfitting.

关键词： Data models Noise Noise reduction signal processing algorithms Knowledge engineering Prediction algorithms Noise measurement Heuristic algorithms Frequency-domain analysis Filtering algorithms Attention mechanism denoising filtering algorithm knowledge tracing (KT)

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Distributed Cascaded Spline-Based Adaptive Graph Filters for Nonlinear Systems: Design and Performance Analysis

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2025年 11卷 274-288页

作者： Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Guo, Zhongyuan Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

The distributed nonlinear adaptive graph filter (DNAGF) is developed with the single nonlinear graph filter model (NGFM) to handle streaming datasets. However, the current DNAGFs tend to underperform when predicting unknown nonlinear dynamic systems. This suboptimal performance is due to their reliance on a single NGFM and the network's limited computational burden. To address these issues, two novel cascaded DNAGFs considering the spline interpolation method, i.e. a distributed Wiener spline adaptive graph filter (DWSAGF) and distributed Hammerstein spline adaptive graph filter (DHSAGF), are proposed to improve the capacity for nonlineaprediction in this paper. By utilizing piecewise low-order nonlinear spline functions, the proposed DWSAGF and DHSAGF can adapt locally to improve the fitting of the predicted nonlinear system to the unknown one. In DWSAGF and DHSAGF, the cascaded architectures containing linear and nonlinear subsystems are employed, which are more flexible than the single NGFM. Particularly, since DHSAGF has a memory through the constructed matrix U(t)(m)(r), it generates higher performance than DWSAGF for complex or time-varying nonlinear systems. In addition, the detailed performance analyses regarding DWSAGF and DHSAGF in the mean and mean-square senses are presented. Simulations are exhibited to validate the theoretical analysis and to show the performance superiorities of DWSAGF and DHSAGF.

关键词： Splines (mathematics) Adaptation models Interpolation Autoregressive processes Vectors Polynomials Nonlinear systems Finite impulse response filters signal processing algorithms Information processing Cascaded models distributed estimation graph signal processing nonlinear graph filter spline adaptive filter

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A Robust Timing Recovery Algorithm With Enhanced DPLL Performance for Nyquist Digital Multi-Band Optical Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第1期43卷 29-41页

作者： Zhang, Ziheng Guo, Wanzhen Luo, Jiating Zheng, Bofang Zhao, Jian South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China

We propose a novel timing recovery algorithm (TRA) for Nyquist digital multi-band optical coherent systems. The proposed TRA employs two complementary metrics to cover all states of polarization (SOP) and differential group delays (DGD), and the extracted timing error is also robust to residual chromatic dispersion (rCD), phase noise and residual frequency offset (RFO). 1.12-Tbit/s simulations and 320-Gbit/s experiments are conducted to verify the proposed theory. It is shown that compared to the conventional Gardner, Godard and sGardner algorithms, the proposed TRA eliminates the singularity problem when the DGD is equal to odd times of half symbol period and the susceptibility to the signal spectral roll-off factor. This method also enhances the performance of the digital phase-lock loop with higher tolerance to clock frequency offset and loop delay than the conventional TRAs and our recently proposed TRA.

关键词： Delays Phase noise signal processing algorithms Symbols Receivers Clocks Transmitters Differential group delay digital multi-band system digital phase-lock loop time recovery

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Simplified Fast List PAC Decoder for Broadcasting Services in 6G: Algorithm and Implementation

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IEEE TRANSACTIONS ON BROADCASTING 2025年

作者： Dai, Jingxin Yin, Hang Lv, Yansong Wang, Yuhuan Xu, Yin Lv, Rui Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai 200240 Peoples R China

In the 6G network, integrating broadcasting and mobile networks will significantly improve the transmission capability. Considering the excellent error-correction performance, polarized-adjusted convolutional (PAC) codes are promising for ensuring reliable data transmission in 6G broadcasting services. However, the inherent high decoding latency of PAC codes poses challenges for seamless switching between broadcasting and mobile services. In this paper, we propose a simplified fast list (SFL) PAC decoder, which jointly exploits the node thresholds and adaptive path-pruning technology to reduce the decoding latency while maintaining high reliability. Firstly, we present a novel path expansion rule based on the node thresholds to avoid unnecessary computations. Secondly, the introduction of the adaptive path-pruning technology efficiently reduces the number of sorting operations. Moreover, we implement the proposed decoder on general purpose processors (GPPs) by software. Simulation results show that the proposed SFL decoding algorithm significantly reduces the decoding latency by up to 75.18% compared to the state-of-the-art (SOTA) work with no noticeable degradation in error-correction performance. Software implementation of the proposed decoder achieves an 18.80% improvement in throughput performance over the SOTA PAC software decoder.

关键词： Decoding Picture archiving and communication systems Software Codes 6G mobile communication Broadcasting Software algorithms Throughput Polar codes signal processing algorithms PAC codes broadcasting decoding latency threshold decoder software

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A NTRU-Based Data Deduplication Scheme for Underwater Acoustic Sensor Networks

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IEEE SENSORS JOURNAL 2025年第7期25卷 12210-12221页

作者： Xu, Ming Guo, Tongtong Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Data security and storage capacity have become more important as a result of the extensive use of underwater acoustic sensor networks (UWSNs) in fields including military applications, national defense, and marine resource research. To maintain data security while cutting costs associated with transmission and storage, we propose a number theory research unit (NTRU)-based data deduplication strategy for UWSNs, coined as NTRU-based data deduplication scheme for underwater acoustic sensor networks (NTDP). The NTDP scheme is novel in three aspects. First, a novel encryption scheme is proposed to ensure data security and privacy by utilizing primitive vectors, combined with NTRU multi-key fully homomorphic encryption and certificateless signatures in an underwater resource-constrained environment. Second, we propose a dynamic partitioning algorithm, which can identify repetitions within preprocessed data blocks so as to effectively achieve data deduplication. Third but not least, the NTDP scheme embeds a secure proof of ownership (PoW) protocol to ensure semantic security, enabling surface relay devices to claim data ownership without disclosing the actual data content, thus improving the level of data privacy protection. Extensive experimental results show that the NTDP scheme effectively enhances data transmission security and improves data storage efficiency in UWSNs.

关键词： Sensors Relays Sea surface Noise Underwater acoustics Heuristic algorithms Data models Unified modeling language Surface treatment signal processing algorithms Dynamic partition number theory research unit (NTRU) proof of ownership (PoW) underwater acoustic sensor networks (UWSNs)

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A Robust VMD-Based Modified Multivariable Output Error Statespace Method for Detection of Low-Frequency Oscillations From Noisy signals of WAMS

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IEEE ACCESS 2025年 13卷 2152-2172页

作者： Shetty, Dinesh Prabhu, Nagesh NITTE NMAM Inst Technol Dept Elect & Elect Engn Nitte 574110 Karnataka India

A poorly damped set of power oscillations interconnected power systems may lead to system-wide breakups or considerably reduce the power transfer over critical channels and corridors. As a result, identifying and suppressing these oscillatory modes is a critical challenge in modern power systems. Advances in wide area monitoring systems in modern power system facilitates the numerous data for the analysis of the system characteristics. Wide Area Monitoring Systems (WAMS) have enabled the collection of large amounts of data, aiding in system analysis. Phasor measurement units may experience noisy signals during measurement which may lead to poor identification of low frequency oscillatory modes or introduce extraneous modes during analysis using state of the art methods. This research examines the performance of the Robust Variable Mode Decomposition (VMD) technique for analyzing signals contaminated with noise. The study employs particle swarm optimization to determine the appropriate quantity of Intrinsic Mode Functions (IMFs). Additionally, it applies Anderson-Darling (AD) and Kolmogorov-Smirnov (KD) statistical distance measures to identify the effective number of IMFs, enabling the differentiation between signal IMFs and noise IMFs. Wavelet de-noising technique is employed for the de-noising the sum of chosen IMF signals. To validate the selection of adequate IMFs a heuristic approach is used to monitor the signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for effectively seperating noisy contents from actual content of the signal. The proposed system incorporates the wavelet based filter in conventional VMD which not only helps in fetching the frequency but also helps in filtering the noise with appropriate selection of the mother wavelet, i.e. it utilizes the fusion of VMD and Wavelet Transform. The approach leverages the adaptability of VMD's decomposition alongside the precise denoising capabilities of WT, resulting in high effectiveness for signals th

关键词： Oscillators Power system stability signal processing algorithms Noise measurement Transforms Noise Wavelet transforms Damping Noise reduction Eigenvalues and eigenfunctions Anderson Darlington intrinsic mode functions low frequency oscillations variable mode decomposition

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