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Fast Recursive Greedy Methods for Sparse signal Recovery

IEEE TRANSACTIONS ON signal processing 2024年 72卷 4381-4394页

作者： Xiang, Min Zhang, Zhenyue Zhejiang Univ Sch Math Sci Hangzhou 310008 Peoples R China Shenzhen MSU BIT Univ MSU BIT SMBU Joint Res Ctr Appl Math Shenzhen 518172 Guangdong Peoples R China

Theory and algorithms of greedy methods for sparse signal recovery using l(0)-minimization are developed. The theoretical analysis shows that l(0)-minimization is more suitable for sparse signal recovery and that the greedy method based on active set updating can solve l(0)-minimization problems under weak conditions. A fast recursive algorithm RASU is given to greedily update the active set with a given sparsity level, and an adaptive strategy is proposed to escape from the local minima achieved by RASU. RASU is further applied to minimize the residual. The algorithm FRASM repeats this procedure, using multiple starting points to enhance the efficiency. A fast algorithm SHTP is proposed to provide the multiple choices for FRASM, and itself is also an efficient algorithm solving the sparse problem. By combining an adaptive updating rule to estimate the sparsity of the sparsest signal, the FRASM is adopted to minimize the sparsity level subject to a residual constraint, yielding the algorithm SASSP. Numerical experiments demonstrate the superior performance of FRASM and SASSP compared to other sparse algorithms.

关键词： Indexes Matching pursuit algorithms Thresholding (Imaging) signal processing algorithms Iterative methods Vectors Convergence Sparse signal recovery fast recursion active set updating adaptive pursuing

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Towards Inversion-Free Sparse Bayesian Learning: A Universal Approach

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4992-5006页

作者： Song, Yuhui Gong, Zijun Chen, Yuanzhu Li, Cheng Mem Univ Dept Comp Sci St John NF A1B 3X5 Canada Hong Kong Univ Sci & Technol HKUST Guangzhou IoT Thrust Guangzhou 511453 Peoples R China HKUST Dept ECE Hong Kong 999077 Peoples R China Queens Univ Sch Comp Kingston ON K7L 2N8 Canada Simon Fraser Univ Sch Engn Sci Vancouver BC V5A 1S6 Canada Mem Univ Elect & Comp Engn St John NF A1B 3X5 Canada

Sparse Bayesian Learning (SBL) has emerged as a powerful tool for sparse signal recovery, due to its superior performance. However, the practical implementation of SBL faces a significant computational complexity associated with matrix inversion. Despite numerous efforts to alleviate this issue, existing methods are often limited to specifically structured sparse signals. This paper aims to provide a universal inversion-free approach to accelerate existing SBL algorithms. We unify the optimization of SBL variants with different priors within the expectation-maximization (EM) framework, where a lower bound of the likelihood function is maximized. Due to the linear Gaussian model foundation of SBL, updating this lower bound requires maximizing a quadratic function, which involves matrix inversion. Thus, we employ the minorization-maximization (MM) framework to derive two novel lower bounds that diagonalize the quadratic coefficient matrix, thereby eliminating the need for any matrix inversions. We further investigate their properties, including convergence guarantees under the MM framework and the slow convergence rate due to reduced curvature. The proposed approach is applicable to various types of structured sparse signals, such as row-sparse, block-sparse, and burst-sparse signals. Our simulations on synthetic and real data demonstrate remarkably shorter running time compared to state-of-the-art methods while achieving comparable recovery performance.

关键词： signal processing algorithms Inference algorithms Sparse matrices Vectors Computational modeling Complexity theory Convergence Brain modeling Dictionaries Bayes methods Compressed sensing sparse Bayesian learning inversion-free expectation-maximization (EM) algorithm minorization-maximization (MM) algorithm

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Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph signals

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 4331-4345页

作者： Wan, Liangtian Fu, Zhengqiang Ling, Yi Sun, Yuchen Li, Xiaona Sun, Lu Xia, Feng Yan, Xiaoran Aggarwal, Charu C. Dalian Univ Technol DUT Sch Software Technol Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116620 Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian 116620 Peoples R China Amazon Atlanta GA 30337 USA Dalian Maritime Univ Inst Informat Sci Technol Dept Commun Engn Dalian 116026 Peoples R China RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia Zhejiang Lab Res Inst Artificial Intelligence Hangzhou 311121 Peoples R China IBM T J Watson Res Ctr Hawthorne NY 10598 USA

Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework "Z-NetMF," which generalizes random walk samplers to Z-Laplacian graph filters, leading to embedding algorithms with interpretable parameters. In particular, by controlling biases in the time domain, we propose the Z-NetMF-t algorithm, making it possible to scale contributions of random walks of different length. Inspired by node2vec, we design the Z-NetMF-g algorithm, capturing the random walk biases in the graph domain. Moreover, we evaluate the effect of the bias parameters based on node classification and link prediction tasks. The results show that our algorithms, especially the combined model Z-NetMF-gt with biases in both domains, outperform the state-of-art methods while providing interpretable insights at the same time. Finally, we discuss future directions of the Z-NetMF framework.

关键词： Matrix decomposition signal processing algorithms Task analysis Information filters Electronic mail Sun Sparse matrices Biased random walk graph laplacian link prediction matrix factorization network embedding node classification

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An Open, Modular, Ultrasound Digitial signal processing Specification

An Open, Modular, Ultrasound Digitial Signal Processing Spec...

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IEEE International Ultrasonics Symposium (IUS)

作者： Clegg, Harry R. Carpenter, Thomas M. Freear, Steven Cowell, David M. J. Univ Leeds Sch Elect & Elect Engn Ultrasound Grp Leeds W Yorkshire England

ISBN: (数字)9781665466578

ISBN: (纸本)9781665466578

Digital signal processing is used in almost all ultrasound research with groups tending to develop their own software, using different data formats and programming languages. Existing works demonstrate open approaches to digital signal processing, such as the Ultrasound File Format based UltraSound ToolBox. Such tools are highly valuable as they ease collaboration and improve reproducibility, but they tend to he tailored to a specific application and may not translate easily to other ultrasound tasks and modalities. As DSP problems are often drawn from a pool of common operations, this work aims to develop an approach using smaller modules to allow greater flexibility. A specification defining a combined processing framework and data format is presented. Existing implementations are presented in both MATLAB and C++, and some of the features the platform enables are discussed. The proposed specification and implementations are intended to be open source and to allow extensive collaboration between groups.

关键词： Ultrasonic imaging Collaboration signal processing algorithms Digital signal processing Computer architecture C++ languages Software

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DOA Estimation Method Based on Space-Time Coding Antennas With Orthogonal Codes

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第2期72卷 1173-1181页

作者： Fang, Zuqi Zhou, Qunyan Dai, Jun Yan Qi, Zhen Jie Zhang, Jianan Cheng, Qiang Cui, Tie Jun Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Recently, direction-of-arrival (DOA) estimation has been widely employed in wireless communication networks, electromagnetic (EM) environment management, and radar tracking. However, traditional DOA estimation algorithms and systems are based on multiple digital receivers with expensive hardware. To overcome this challenge, we propose a novel DOA estimation method based on a space-time coding antenna. With the aid of space-time coding antenna and orthogonal codes, the received signal of each antenna element can be reproduced with high accuracy, helping to obtain more information on the incident waves. In this case, traditional super-resolved DOA estimation algorithms can be employed to estimate more complicated signals using the space-time coding antenna. Compared to the previous studies on similar accuracy, the proposed method has lower complexity and higher flexibility due to the reproduction of complete element information and the use of multiple signal classification (MUSIC) algorithm. The method can operate in the azimuth angle between -60(degrees) and 60(degrees), with angle estimation errors of less than 1.5(degrees), the estimation RMSE of 0.7(degrees), and high reliability for different modulated signals.

关键词： Estimation Direction-of-arrival estimation Encoding Antennas signal processing algorithms Antenna measurements Receiving antennas Antenna array direction of arrival (DOA) estimation multiple signal classification (MUSIC) algorithm orthogonal codes space-time coding

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Complex-Valued Projection Statistics for Hybrid Power System State Estimation

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 6052-6062页

作者： Resende, Eduardo M. Pires, Robson Mili, Lamine Univ Fed Itajuba Inst Elect Syst Energy ISEE BR-37500903 Itajuba Brazil Virginia Tech Natl Capital Reg Falls Church Dept Elect Comp Engn Falls Church VA 22043 USA

This paper develops a complex-valued Schweppe-type GM-estimator with the Huber cost function (known as SHGM estimator) that processes both phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) measurements. It is based on the projection statistics (PS) implemented in the complex domain. It is shown that the PS algorithm applied to a real- or a complex-valued Jacobian matrix provides different numerical results and thereby, different set of leverage points. Consequently, both approaches lead to different state estimates. It is demonstrated through simulations carried out on various test systems that the proposed estimator is more reliable and robust than its real-valued counterpart.

关键词： Phasor measurement units Current measurement Voltage measurement Covariance matrices signal processing algorithms State estimation Jacobian matrices Wirtinger calculus complex-valued projection statistics Power system hybrid state estimation

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3-D Deployment of UAV-BSs for Effective Communication Coverage

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IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 25162-25172页

作者： Zeng, Qian Jia, Yuheng Li, Chuanqi Liu, Liyuan Nanning Normal Univ Demonstrat Modern Ind Sch Guangxi Key Lab Funct Informat Mat & Intelligent I Smart Logist Ind Sch Construct ProjectGuangxi Uni Nanning 530100 Peoples R China

In the field of unmanned aerial vehicles (UAVs), their potential as aerial base stations for post-disaster communication recovery or providing communication coverage in remote areas has gained global recognition. However, the challenge of striking a balance between maximizing coverage and minimizing energy consumption persists. To address this issue, we introduce a novel distributed 3-D deployment approach for UAV-based base stations (UAV-BSs) called 3-D deployment for effective communication coverage (DECC). First, the 3-D deployment problem is decoupled into a horizontal placement subproblem and an altitude determination subproblem. Then, we propose an enhanced genetic algorithm to obtain the optimal horizontal location of UAV-BSs, which incorporates adaptive two-point crossover and an inferior value replacement strategy. Finally, a gray wolf optimization with the dynamic weight is adopted to determine the altitude to optimize the transmit power while ensuring quality of service for the users. The simulation results demonstrate that the proposed algorithm can get a better coverage compared to the current deployment strategies.

关键词： Three-dimensional displays Autonomous aerial vehicles Optimization Energy consumption Wireless communication signal to noise ratio signal processing algorithms 3-D deployment genetic algorithm gray wolf optimization (GWO) unmanned aerial vehicle-based base stations (UAV-BSs) wireless communications

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Blind Polarization Demultiplexing of Shaped QAM signals Assisted by Temporal Correlations

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第2期42卷 560-571页

作者： Bajaj, Vinod Van de Plas, Raf Wahls, Sander Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Vanderbilt Univ Mass Spectrometry Res Ctr Nashville TN 37240 USA Vanderbilt Univ Dept Biochem Nashville TN 37240 USA Karlsruhe Inst Technol KIT Inst Ind Informat Technol Karlsruhe TN Germany

While probabilistic constellation shaping (PCS) enables rate and reach adaption with finer granularity [1] (Cho and Winzer, 2009), it imposes signal processing challenges at the receiver. Since the distribution of PCS-quadrature amplitude modulation (QAM) signals tends to be Gaussian, conventional blind polarization demultiplexing algorithms are not suitable for them [2] (Johnson et al., 1998). It is known that independently and identically distributed (iid) Gaussian signals, when mixed, cannot be recovered/separated from their mixture. For PCS-QAM signals, there are algorithms such as [3] and [4] Dris et al. (2019) and Athuraliya et al. (2004) which are designed by extending conventional blind algorithms used for uniform QAM signals. In these algorithms, an initialization point is obtained by processing only a part of the mixed signal, which have non-Gaussian statistics. In this article, we propose an alternative method wherein we add temporal correlations at the transmitter, which are subsequently exploited at the receiver in order to separate the polarizations. We will refer to the proposed method as frequency domain (FD) joint diagonalization (JD) probability aware-multi modulus algorithm (pr-MMA), and it is suited to channels with moderate polarization mode dispersion (PMD) effects. Furthermore, we extend our previously proposed JD-MMA [5] (Bajaj et al., 2022) by replacing the standard MMA with a pr-MMA, improving its performance. Both FDJD-pr-MMA and JD-pr-MMA are evaluated for a diverse range of PCS (entropy {H ) of 64-QAM over a first-order PMD channel that is simulated in a proof-of-concept setup. A MMA initialized with a memoryless constant modulus algorithm (CMA) is used as a benchmark. We show that at a differential group delay (DGD) of 10% of symbol period T (symb) and 18 dB SNR/pol., JD-pr-MMA successfully demultiplexes the PCS signals, while CMA-MMA fails drastically. Furthermore, we demonstrate that the newly proposed FDJD-pr-MMA is robust against mo

关键词： signal processing algorithms Symbols Quadrature amplitude modulation Demultiplexing Correlation Optical fiber dispersion Programmable logic arrays Digital signal processing optical fiber communication polarization demultiplexing probabilistic constellation shaping

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Near-Field Channel Estimation for XL-MIMO Systems Using Variational Bayesian Learning

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第9期23卷 10740-10756页

作者： Pisharody, Jayanth N. Rajoriya, Anupama Budhiraja, Rohit Indian Inst Technol IIT Kanpur Dept Elect Engn Kanpur 208016 India

We estimate the uplink channel of an extra largescale multiple-input-multiple-output (XL-MIMO) system, with the base station (BS) consisting of multiple antenna sub-arrays. The BS has a decentralized processing architecture, wherein each sub-array is connected to a local processing unit (LPU), which performs its signal processing tasks. The non-stationarity in XL-MIMO channel causes it to become sparse. We design a decentralized channel estimation algorithm to exploit this sparsity. In this algorithm, each LPU decentrally estimates the channel of a sub-array by exchanging information with other LPUs. This exchange is required to exploit the XL-MIMO sparsity. This decentralized algorithm is designed in two steps. The first step designs a centralized sub-array-based variational Bayesian learning (cS-VBL) algorithm. The second step extends this centralized design to a decentralized S-VBL (dS-VBL) implementation, which converts the centralized hyperparameter updates in cS-VBL algorithm to equivalent decentralized optimization problems. Each LPU decentrally solves this problem using asynchronous alternating direction method of multipliers. We show that the proposed i) cS-VBL and dS-VBL algorithms exploit the XL-MIMO channel sparsity, and outperform their existing centralized and decentralized counterparts;and ii) dS-VBL algorithm is robust to LPU failures, and has a lower complexity than cS-VBL algorithm.

关键词： Channel estimation Bayes methods Antenna arrays Inference algorithms Antennas Wireless communication signal processing algorithms Decentralized architecture extra large-scale multiple-input-multiple-output (XL-MIMO) variational Bayesian learning (VBL)

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Stable Outlier-Robust signal Recovery Over Networks: A Convex Analytic Approach Using Minimax Concave Loss

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2024年 10卷 690-705页

作者： Tillmann, Maximilian H. V. Yukawa, Masahiro Keio Univ Dept Elect & Elect Engn Yokohama Kanagawa 2238522 Japan Univ Bremen Dept Commun Engn D-28359 Bremen Germany

This paper presents a mathematically rigorous framework of remarkably-robust signal recovery over networks. The proposed framework is based on the minimax concave (MC) loss, which is a weakly convex function so that it attains i) remarkable outlier-robustness and ii) guarantee of convergence to a solution of the posed problem. We present a novel problem formulation which involves an auxiliary vector so that the formulation accommodates statistical properties of signal, noise, and outliers. We show the conditions to guarantee convexity of the local and global objectives. Via reformulation, the distributed triangularly preconditioned primal-dual algorithm is applied to the posed problem. The numerical examples show that our proposed formulation exhibits remarkable robustness under devastating outliers as well as outperforming the existing methods. Comparisons between the local and global convexity conditions are also presented.

关键词： Vectors Robustness Convergence Gaussian noise Information processing signal processing algorithms Perturbation methods Distributed optimization outlier robustness minimax concave penalty proximity operator

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