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Distributed Proximal Alternating Direction Method of Multipliers for Constrained Composite Optimization Over Directed Networks

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2024年 10卷 539-551页

作者： Yan, Jing Shi, Xinli Guo, Luyao Wan, Ying Wen, Guanghui Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

In this article, we investigate a constrained composition optimization problem in a directed communication network. Each agent is equipped with a local objective function composed of both smooth and nonsmooth terms, as well as linear equality constraints. The optimization objective is to minimize the sum of all local functions, subject to linear equality constraints, through local computations and information exchange with neighboring agents. Based on the alternating direction method of multipliers (ADMM), a novel distributed optimization algorithm is proposed to address the composite optimization problem. We leverage the composite structure of the objective function, by introducing a linear approximation for the smooth term and a proximal mapping for the nonsmooth term, which simplifies the process of solving the ADMM subproblem. Furthermore, in contrast to the existing algorithms that eliminate the imbalance resulting from directed graphs using a column-stochastic matrix, the proposed algorithm only employs a row-stochastic matrix, thereby avoiding the need for agents to know their outdegree. Moreover, the step sizes of agents are uncoordinated and can be independent of the network topology. Furthermore, we prove that the proposed algorithm achieves a sublinear convergence rate when the local objective functions are convex. Finally, the effectiveness of the proposed algorithm is verified through numerical simulations.

关键词： Optimization Convergence signal processing algorithms Convex functions Linear programming Vectors Distributed composition optimization constrained optimization alternating direction method of multipliers (ADMM) directed graphs finite-time consensus

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Simultaneous Multi-Beam Pattern Synthesis of Subarray via Alternating Projection

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IEEE ACCESS 2024年 12卷 66352-66362页

作者： Jing, Zhongtian Han, Yubing Feng, Yuchen Xiao, Lei Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China

For a large-scale array, the subarray technology can save the radio frequency (RF) channels. However, the use of subarray technology often tends to reduce the beamforming performance of the array. In some specific scenarios, it is necessary to use simultaneous multi-beam technology for target search. Exploring methods to simultaneously achieve multi-beamforming while reducing radio frequency channels is a worthwhile research endeavor. This paper introduces an innovative application of hybrid beamforming structure to the realm of beam synthesis and proposes an algorithm for simultaneous multi-beam pattern synthesis in subarrays. This method weights the received signal of the array twice. Initially, the analog signal received by each antenna unit undergoes the first weighted, accomplished by incorporating a phase shifter after each antenna. After the initial weighted, the analog signals proceed into their respective subarray channels, where they undergo a second step of weighted after being converted into digital signals. The second step of weighted involves digital beamforming of the subarray signals, utilizing multiple sets of weights, to achieve simultaneous multibeam. The algorithm can obtain an element weighted vector and a subarray weighted matrix. Based on the alternating projection (AP) algorithm, the property of the Kronecker product, and the definition of the Khatri-Rao product, the optimal element weighted vector and subarray weighted matrix can be obtained by solving the 2-norm minimization problem.

关键词： Vectors Array signal processing Phase shifters Adaptive arrays signal processing algorithms Radio frequency Subarray synthesis algorithm multiple simultaneous patterns alternating projection

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A Comprehensive Review of Recent Advances on Intelligence algorithms and Information Engineering Applications

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IEEE ACCESS 2024年 12卷 135886-135912页

作者： Zeng, Yang Liao, Bolin Li, Zhan Hua, Cheng Li, Shuai Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Jishou Univ Sch Commun & Elect Engn Jishou 416000 Peoples R China Southwest Jiaotong Univ Inst Smart City & Intelligent Transportat Chengdu 610031 Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland VTT Technol Res Ctr Finland Oulu 90570 Finland

The advent of artificial intelligence (AI) has catalyzed transformative changes across diverse domains, establishing itself as a disruptive technology. Traditional methods often fall short in solving intricate information engineering problems, whereas AI methods can offer superior efficiency, speed, and accuracy. This paper provides a comprehensive overview of the research and practical applications of artificial intelligence in addressing information engineering challenges over the past two decades. It focuses on four critical information engineering fields: intelligent control, pattern recognition and intelligent systems, intelligent signal processing and communication, and optimization algorithms. This paper covers the backgrounds, driving forces, technologies, and applications of these fields, as well as the applications of AI in these areas. We believe that this study will be a valuable resource for both novice and seasoned researchers interested in leveraging AI to tackle information engineering challenges, offering a comprehensive understanding of AI integration and its potential in these areas.

关键词： Mathematical models Neural networks Biological neural networks Convergence signal processing algorithms Reviews Linear systems Artificial intelligence Intelligent control Optimization methods neural networks intelligent control intelligent optimization

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Learning Domain-Invariant Model for WiFi-Based Indoor Localization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 13898-13913页

作者： Wang, Guanzhong Zhang, Dongheng Zhang, Tianyu Yang, Shuai Sun, Qibin Chen, Yan Univ Sci & Technol China Sch Cyber Sci & Technol Hefei 230027 Peoples R China Inst Artificial Intelligence Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Hefei Peoples R China

WiFi-based indoor localization has gained widespread attention due to the pervasive availability of WiFi Access Points (APs). While signal processing-based methods can achieve decimeter-level localization, their performance is constrained by the limited spatial resolution of WiFi systems, especially in complex environments with strong interference. By contrast, deep learning-based methods have achieved impressive performance even in complex environments, which however often fail to generalize to new environments. In this paper, we propose a novel framework to learn domain-invariant model for WiFi-based indoor localization, which maintains impressive performance across different environments. The key insight is to design a deep learning-based WiFi localization system through the perspective of signal processing. Specifically, we let the neural network estimate APs-centered polar coordinates to avoid fitting the coordinates of APs strongly correlated with the environment, enabling us to obtain the domain-invariant model. To unleash the potential of neural networks in regressing high-precision parameters, we design a beamforming layer to integrate the knowledge of signal processing. Furthermore, we propose a multi-task learning scheme to further improve localization accuracy. Extensive experiments on diverse datasets have demonstrated that the localization performance of our method outperforms state-of-the-art methods and demonstrates superiority under cross-domain conditions.

关键词： Location awareness Wireless fidelity Accuracy Performance evaluation signal processing algorithms Learning systems Feature extraction Channel state information deep learning indoor localization WiFi

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Hypformer: A Fast Hypothesis-Driven Rescoring Speech Recognition Framework

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IEEE signal processing LETTERS 2025年 32卷 471-475页

作者： Zhuang, Xuyi Qian, Yukun Wang, Mingjiang Harbin Inst Technol Shenzhen Shenzhen 518000 Peoples R China

Recently, the performance of non-autoregressive ASR models has made significant progress but still lags behind hybrid CTC/attention systems. This paper introduces Hypformer, a fast hypothesis-driven rescoring speech recognition framework. Multiple hypothetical prefixes are realized by fast prefix generation algorithm. With two different rescoring methods, nar-ar rescoring and nar$<^>{2}$ rescoring, Hypformer can flexibly switch between autoregressive and non-autoregressive decoding modes to perform rescoring of hypothesis prefixes. Experiments on the standard Mandarin datasets AISHELL-1 and AISHELL-2 demonstrate that Hypformer outperforms the state-of-the-art Hybrid CTC/Attention systems in ASR performance while achieving a speedup of over six times. Experiments on the Mandarin sub-dialect dataset KeSpeech indicate that Hypformer achieves more accurate recognition by leveraging richer contextual information.

关键词： Decoding Training Iterative decoding Transformers Accuracy Standards Symbols Switches signal processing algorithms Probability distribution ASR E2E non-autoregressive hypothesis prefixes rescoring

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l0 Penalized Maximum Likelihood Estimation of Sparse Covariance Matrices

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IEEE signal processing LETTERS 2025年 32卷 66-70页

作者： Fatima, Ghania Stoica, Petre Babu, Prabhu Indian Inst Technol Delhi CARE New Delhi 110016 India Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

In this letter we present a framework for estimating sparse covariance matrices, wherein we solve the l(0)-norm penalized maximum likelihood estimation problem using the extended Bayesian information criterion (EBIC), a high dimensional model selection rule. The framework combines choosing the sparsity pattern and estimating the covariance matrix in a single step, eliminating the need for any hyper-parameter tuning. Using the framework we propose a cyclic majorization-minimization based technique and apply it to synthetic data to evaluate its performance in terms of normalized root mean square error (NRMSE) and Kullback Leibler (KL) divergence.

关键词： Covariance matrices Maximum likelihood estimation Sparse matrices Minimization Tuning signal processing algorithms Optimization Bayes methods Wireless communication Taylor series Covariance matrix estimation EBIC l(0) penalized maximum likelihood

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FastPSA: A Fast Version of the Principal Skewness Analysis

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IEEE signal processing LETTERS 2025年 32卷 1226-1230页

作者： Gao, Jingyu Geng, Xiurui Ji, Luyan Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA does not require all sample points when searching for the projection directions, making it faster. However, for the data of dimension L, PSA needs to calculate the eigenvectors of L tensors, each of size LxLxL. When L is large, PSA still requires significant computational time to find all projection directions. In this letter, we find that the (m+1)th projection direction in PSA can be obtained by calculating the eigenvector of a tensor of size (L-m) x (L-m) x (L-m). Furthermore, we propose a fast version of PSA (FastPSA) that is mathematically equivalent to PSA. The experimental results demonstrate that FastPSA has lower computational complexity than PSA.

关键词： Tensors Principal component analysis Feature extraction Computational complexity Vectors signal processing algorithms Training Hyperspectral imaging Data mining Computational efficiency FastICA principal skewness analysis hyperspectral image

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A Robust Adaptive Beamforming Algorithm for Mismatch and Impulsive Noise Circumstance

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Xuejun Feng, Dazheng Wang, Zhonggen Gan, Fubao Anhui Univ Sci & Technol Sch Elect & Informat Huainan 232001 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This letter provides a solution to a robust adaptive beamformer used in remote sensing systems under the effect of two factors: 1) the impulsive noise satisfying Gaussian mixture distribution and 2) unknown model mismatches, such as look direction errors and sensor location perturbations. First, a beamformer output-based cost function is designed to effectively suppress the influence of impulsive noise. Second, the weight vector of the beamformer is regularized through norm constraints to reduce the impact of model mismatches. Furthermore, an efficient iterative scheme is established to quickly minimize the designed cost function based on their properties, and a robust adaptive beamformer can be obtained. Finally, computer simulations are presented to demonstrate the robust performance and fast convergence of the novel algorithm in the presence of impulsive noise and model mismatches.

关键词： Noise Cost function Array signal processing Adaptation models Vectors Loading Interference signal to noise ratio Costs signal processing algorithms impulsive noise model mismatch regularization robust adaptive beamformer

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A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

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IEEE signal processing LETTERS 2025年 32卷 1246-1250页

作者： Du, Yuxi Cui, Weijia Ba, Bin Chen, Yangyang Zhang, Long PLA Strateg Support Informat Engn Univ Zhengzhou 450001 Peoples R China

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

关键词： Interference Antenna arrays signal to noise ratio Array signal processing Noise Vectors Computational complexity signal processing algorithms Covariance matrices Receiving antennas Two-level weighted adaptive beamforming signal to interference plus noise ratio

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Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34409-34419页

作者： Li, Xiang Yuan, Weijie Zhang, Kecheng Wu, Nan Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Robot & Comp Vis Shenzhen 518055 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using EL methods, several approaches have been proposed combining message passing (MP) algorithms. In this article, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low-complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.

关键词： Estimation Robot sensing systems Location awareness Accuracy signal processing algorithms Message passing Internet of Things Consensus algorithm distributed estimation edge learning (EL) factor graph Gaussian mixture model (GMM) message passing (MP)

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