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Iterative Difference Hard-Thresholding Algorithm for Sparse signal Recovery

IEEE TRANSACTIONS ON signal processing 2023年 71卷 1093-1102页

作者： Cui, Angang He, Haizhen Xie, Zhiqi Yan, Weijun Yang, Hong Yulin Univ Sch Math & Stat Yulin 719000 Peoples R China Yulin Univ Sch Int Educ Yulin 719000 Peoples R China

In this paper, a nonconvex surrogate function, namely, Laplace norm, is studied to recover the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of $l_{0}$-norm minimization problem, Laplace norm minimization problem and regularization Laplace norm minimization problem. It is proved that the $l_{0}$-norm minimization problem can be solved by solving the regularization Laplace norm minimization problem if the certain conditions are satisfied. Secondly, an iterative difference hard-thresholding algorithm and its adaptive version algorithm are proposed to solve the regularization Laplace norm minimization problem. Finally, we provide some numerical experiments to test the performance of the adaptive iterative difference hard-thresholding algorithm, and the numerical results show that the adaptive iterative difference hard-thresholding algorithm performs better than some state-of-art methods in recovering the sparse signals.

关键词： Minimization signal processing algorithms Iterative algorithms Thresholding (Imaging) Indexes Eigenvalues and eigenfunctions Linear matrix inequalities Laplace norm equivalence iterative difference hard-thresholding algorithm adaptive iterative difference hard-thresholding algorithm

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Active Terminal Identification, Channel Estimation, and signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第4期22卷 2847-2866页

作者： Zhou, Xingyu Ying, Keke Gao, Zhen Wu, Yongpeng Xiao, Zhenyu Chatzinotas, Symeon Yuan, Jinhong Ottersten, Bjorn Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol MIIT Key Lab Complex Field Intelligent Sensing Beijing 100081 Peoples R China Yangtze Delta Reg Acad Beijing Inst Technol Jiaxin Jiaxing 314019 Peoples R China Adv Technol Res Inst Beijing Inst Technol Jinan 250307 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust L-1855 Luxembourg Luxembourg Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2025 Australia

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial-satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

关键词： OFDM Low earth orbit satellites Rayleigh channels signal processing algorithms Broadband communication Narrowband Frequency measurement Internet of Things (IoT) low earth orbit (LEO) satellite orthogonal time frequency space (OTFS) grant-free non-orthogonal multiple access (GF-NOMA)

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Speech Enhancement Algorithm Based on a Convolutional Neural Network Reconstruction of the Temporal Envelope of Speech in Noisy Environments

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IEEE ACCESS 2023年 11卷 5328-5336页

作者： Soleymanpour, Rahim Soleymanpour, Mohammad Brammer, Anthony J. Johnson, Michael T. Kim, Insoo Univ Connecticut Dept Med Sch Med Farmington CT 06030 USA Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Univ Kentucky Dept Elect & Comp Engn Lexington KY 40506 USA

Temporal modulation processing is a promising technique for improving the intelligibility and quality of speech in noise. We propose a speech enhancement algorithm that constructs the temporal envelope (TEV) in the time-frequency domain by means of an embedded convolutional neural network (CNN). To accomplish this, the input speech signals are divided into sixteen parallel frequency bands (subbands) with bandwidths approximating 1.5 times that of auditory filters. The corrupted TEVs in each subband are extracted and then fed to the 1-dimensional CNN (1-D CNN) model to restore the TEVs distorted by noise. The method is evaluated using 2,700 words from nine different talkers, which are mixed with speech-spectrum shaped random noise (SSN), and babble noise, at different signal-to-noise ratios. The Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) metrics are used to evaluate the performance of the 1-D CNN algorithm. Results suggest that the 1-D CNN model improves STOI scores on average by 27% and 34% for SSN and babble noise, respectively, and PESQ scores on average by 19% and 18%, respectively, compared to unprocessed speech. The 1-D CNN model is also shown to outperform a conventional TEV-based speech enhancement algorithm.

关键词： Speech enhancement Noise measurement signal processing algorithms Convolutional neural networks Psychoacoustic models Time-frequency analysis temporal envelope (TEV) convolution neural network (CNN)

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Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第2期17卷 445-457页

作者： Zhang, Bangjie Xu, Gang Zhou, Rui Zhang, Hui Hong, Wei Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Due to the low-cost and compact size, millimeter-wave radar is gaining increasing attention in automotive applications. Compared with the array imaging technique, synthetic aperture radar (SAR) takes advantage of high resolution, low system complexity, and is an important powerful tool in automotive radar imaging. In this paper, a high-resolution MIMO SAR imaging algorithm is proposed in the framework of Doppler-division multiplexing (DDM), where a novel multi-channel back-projection (BP) approach is presented for coherent multi-channel image integration. In the scheme, the DDM is applied on transmitter and Doppler filtering is employed to separate echoes of different transmitters on one receiver. Then the proposed multi-channel BP algorithm is divided into two parts. The first step corresponds to the BP SAR imaging on each channel, effectively dealing with the problem of near-field and wide-angle focusing. As follows, digital beamforming (DBF) and beampattern synthesis based on alternating direction method of multipliers (ADMM) is used for multi-channel SAR image coherent fusion. Experiments using real-measured data from an automotive radar system with a two-transmitter-eight-receiver array validate the effectiveness of the proposed algorithm.

关键词： Radar imaging Imaging Radar Synthetic aperture radar MIMO communication Automotive engineering signal processing algorithms Automotive synthetic aperture radar (SAR) Doppler-division multiplexing (DDM) back-projection (BP) digital beamforming (DBF)

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A Joint Model Extraction and Data Detection Framework for IRS-NOMA System

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 164-177页

作者： Yang, Zikun Li, Feng Zhang, Dou Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Peoples R China

This paper addresses the problem of data detection for intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. Also, the phase noise, analog-to-digital converter (ADC) quantization error and imperfect channel estimation are taken into consideration. At present, IRS is designed to be deployed in a wide range of scenarios. However, the quantization error model and IRS phase error model developed for limited scenarios may not be well adapted to the complex and changing environment, which brings a new challenge for the application of IRS. Therefore, this paper investigates a new hybrid method for joint noise model extraction, device activity detection and data detection in the case of unknown noise models, to solve the aforementioned IRS problem. Firstly, the Gaussian mixture model (GMM) is used to describe the model of quantization noise and phase noise. Thus the problem of model extraction is transformed into the calculation of the key parameters of GMM. Secondly, an improved expectation maximization algorithm is used to calculate the quantization noise model parameters. Finally, utilizing the property of the structure of the problem, a message-passing based algorithm is investigated. Simulation results show the merits of the proposed hybrid algorithm.

关键词： Quantization (signal) Data models signal processing algorithms Phase noise Wireless communication NOMA Channel estimation Intelligent reflecting surface non-orthogonal multiple access model-extraction data detection meassage passing

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Gradient-Push Algorithm for Distributed Optimization With Event-Triggered Communications

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IEEE ACCESS 2023年 11卷 517-534页

作者： Kim, Jimyeong Choi, Woocheol Sungkyunkwan Univ Dept Math Suwon 16419 South Korea

Decentralized optimization problems consist of multiple agents connected by a network. The agents have each local cost function, and the goal is to minimize the sum of the functions cooperatively. It requires the agents to communicate with each other, and reducing the cost for communication is desired for a communication-limited environment. Recently, the decentralized gradient descent algorithms involving event-triggered communication have been proposed when the network of the agents is an undirected graph. On the other hand, the network of agents is often directed graph for realistic scenarios whose communication resources are limited. In this work, we first propose a gradient-push algorithm involving event-triggered communication on a directed network. Each agent sends its current states to its neighbors only when the differences between the latest sent states and the current states are larger than thresholds. The convergence of the algorithm is established under suitable decays and summability conditions on a stepsize and triggering thresholds. Numerical experiments are presented to support the effectiveness and the convergence results of the algorithm. More precisely, the numerical results reveals that the proposed algorithm may reduce the communication cost significantly compared to the gradient-push algorithm not involving the event-triggered communication.

关键词： signal processing algorithms Convergence Directed graphs Cost function Distributed databases Costs Event detection Gradient methods Decentralized optimization directed graph event-triggered communication gradient push algorithm

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Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 5978-5992页

作者： Zhang, Guangbin Huang, Tianyao Liu, Yimin Wang, Xiqin Eldar, Yonina C. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Weizmann Inst Sci Dept Comp Sci & Appl Math IL-7610001 Rehovot Israel

We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a multiple-measurement model, and show that there is approximate orthogonality between the source signals in this model. We then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.

关键词： Apertures signal resolution Array signal processing Sensors Transforms signal processing algorithms Sensor arrays Blind source separation high angular resolution orthogonality partly calibrated array

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Gerchberg-Saxton Based FIR Filter for Electronic Dispersion Compensation in IM/DD Transmission Part II: Experimental Demonstration and Analysis

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1428-1435页

作者： Wu, Xiong Karar, Abdullah S. Zhong, Kangping Gurkan, Zeynep Nilhan Lau, Alan Pak Tao Lu, Chao Hong Kong Polytech Univ Photon Res Inst Dept Elect & Informat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Amer Univ Middle East Coll Engn & Technol Egaila 15453 Kuwait Poet Technol Inc Shenzhen 518057 Peoples R China

In this article, the first experimental demonstration of a non-iterative electronic dispersion compensation (EDC) solution implemented at the transmitter using a finite impulse response (FIR) filter optimized with the Gerchberg-Saxton (GS) algorithm, is presented, for intensity-modulation and direct-detection (IM/DD) systems. The theoretical framework and preliminary simulations have been presented in Part I of this work. Here, the performance is compared between the GS FIR filter and the iterative GS algorithm in 56-Gb/s on-off keying (OOK) transmissions over 80-km single mode fiber (SMF) with a post feed-forward equalizer (FFE) for combating residual inter-symbol interference. Furthermore, the influence of the pulse shape (raised cosine or rectangular) and modulation format (return-to-zero (RZ) or non-return-to-zero (NRZ)) on the measured bit error ratio (BER) is investigated, while changing the number of FIR taps, post-FFE taps, and post-FFE samples-per-symbol. It is shown that within the range of the target digital extinction ratios (DERs) for which the original iterative GS algorithm offers benefit, both analytical and numerical methods for calculating the optimum FIR taps, outlined in Part I, produce similar BER performance as predicted. Hence, the former method is extended, here, through a non-recursive frequency response formula, which offers insight into the action of the GS filter with different pulse shaping and enables the derivation of the explicit GS impulse response. It is also shown that rectangular RZ exploits the full benefit of GS filtering through a uniform spectrum, achieving BER < 3.8x10(-3), with a 641-tap T/2-spaced pre-EDC FIR filter and a 3-tap adaptive T-spaced post-FFE.

关键词： Finite impulse response filters Modulation Dispersion Filtering algorithms signal processing algorithms Frequency-domain analysis Transfer functions Digital to analog converter digital signal processing electronic dispersion compensation finite impulse response filter Gerchberg-Saxton algorithm intensity modulation and direct detection Mach-Zehnder modulator

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Statistical Stability of Correlation Based Imaging algorithms

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 327-334页

作者： Leibovich, Matan Papanicolaou, George Tsogka, Chrysoula NYU Courant Inst Math Sci New York NY 10012 USA Stanford Univ Dept Math Stanford CA 94305 USA Univ Calif Merced Appl Math Dept Merced CA 95343 USA

We consider imaging of an airborne target and study the stability of correlation based imaging methods to random fluctuations in the target's motion. The imaging system consists of a ground based emitter, and several passive receivers. By migrating the cross correlations of the received signals the two-point interference matrix is obtained. An image is then formed either by taking the diagonal of this matrix or by computing the eigenvector corresponding to the largest eigenvalue of the matrix. We call the latter the rank-1 imaging method. We show that the rank-1 image exhibits exceptional stability with respect to fluctuations in the platform's motion. We relate that to the stability of the first eigenvector of a matrix perturbed by multiplicative random phase noise. We provide simulations as well as an analytical model that demonstrates the robustness of the rank-1 imaging method to random fluctuations in the target's motion.

关键词： Analytical models Fluctuations Correlation Imaging signal processing algorithms Receivers Radar imaging Inverse synthetic aperture radar radar imaging radar signal processing

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Adaptive CL-BFGS algorithms for Complex-Valued Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 6313-6327页

作者： Zhang, Yongliang Huang, He Shen, Gangxiang Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Jiangsu Engn Res Ctr Novel Opt Fiber Technol & Co Suzhou 215006 Peoples R China

Complex-valued limited-memory BFGS (CL-BFGS) algorithm is efficient for the training of complex-valued neural networks (CVNNs). As an important parameter, the memory size represents the number of saved vector pairs and would essentially affect the performance of the algorithm. However, the determination of a suitable memory size for the CL-BFGS algorithm remains challenging. To deal with this issue, an adaptive method is proposed in which the memory size is allowed to vary during the iteration process. Basically, at each iteration, with the help of multistep quasi-Newton method, an appropriate memory size is chosen from a variable set {1,2,..., M} by approximating complex Hessian matrix as close as possible. To reduce the computational complexity and ensure desired performance, the upper bound M is adjustable according to the moving average of memory sizes found in previous iterations. The proposed adaptive CL-BFGS (ACL-BFGS) algorithm can be efficiently applied for the training of CVNNs. Moreover, it is suggested to take multiple memory sizes to construct the search direction, which further improves the performance of the ACL-BFGS algorithm. Experimental results on some benchmark problems including the pattern classification, complex function approximation, and nonlinear channel equalization problems are given to illustrate the advantages of the developed algorithms over some previous ones.

关键词： Approximation algorithms Training signal processing algorithms Optimization Neural networks Upper bound Mathematical models Adaptive complex-valued limited-memory BFGS (ACL-BFGS) algorithm complex-valued neural networks (CVNNs) moving average multistep quasi-Newton method variable memory size

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