IEEE Transactions on Signal Processing最新文献

{"title":"Symplectic Wigner Distribution in the Linear Canonical Transform Domain: Theory and Application","authors":"Yangfan He, Zhichao Zhang","doi":"10.1109/tsp.2025.3572739","DOIUrl":"https://doi.org/10.1109/tsp.2025.3572739","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"146 1","pages":"1-16"},"PeriodicalIF":5.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Channel Estimation for Beyond Diagonal RIS via Tensor Decomposition","authors":"Andre L. F. de Almeida, Bruno Sokal, Hongyu Li, Bruno Clerckx","doi":"10.1109/tsp.2025.3569802","DOIUrl":"https://doi.org/10.1109/tsp.2025.3569802","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Group Information Geometry Approach for Ultra-Massive MIMO Signal Detection","authors":"Jiyuan Yang;Mingrui Fan;Yan Chen;Xiqi Gao;Xiang-Gen Xia;Dirk Slock","doi":"10.1109/TSP.2025.3561023","DOIUrl":"10.1109/TSP.2025.3561023","url":null,"abstract":"Abstract We propose a group information geometry approach (GIGA) for ultra-massive multiple-input multiple-output (MIMO) signal detection. The signal detection task is framed as computing the approximate marginals of the a posteriori distribution of the transmitted data symbols of all users. With the approximate marginals, we perform the maximization of the a posteriori marginals (MPM) detection to recover the symbol of each user. Based on the information geometry theory and the grouping of the components of the received signal, three types of manifolds are constructed and the approximate a posteriori marginals are obtained through m-projections. The Berry-Esseen theorem is introduced to offer an approximate calculation of the m-projection, while its direct calculation is exponentially complex. In most cases, increasing the number of groups tends to reduce the computational complexity of GIGA. However, when the number of groups exceeds a certain threshold, the complexity of GIGA starts to increase. Simulation results confirm that the proposed GIGA achieves better bit error rate (BER) performance within a small number of iterations, which demonstrates that it can serve as an efficient detection method in ultra-massive MIMO systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2288-2303"},"PeriodicalIF":4.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143893590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic Byzantine Attack on Federated Learning","authors":"Tsung-Hsuan Wang;Po-Ning Chen;Yu-Chih Huang","doi":"10.1109/TSP.2025.3564842","DOIUrl":"10.1109/TSP.2025.3564842","url":null,"abstract":"In this paper, motivated by the severe effects of black-box evasion attacks on machine learning, we investigate the vulnerability of Byzantine attacks to federated learning (FL) systems. Existing studies predominantly evaluate their defense strategies using monotonous Byzantine attacks in the training stage, which fail to consider the public dataset’s characteristics. This oversight may undermine the confidence in Byzantine defense strategies. In this work, we investigate the issue from the perspective of a Byzantine attacker instead of focusing on mitigate Byzantine attacks as a system designer. Adopting a specific learning task as example, we examine it using an optimal probabilistic Byzantine attack policy, which we extend from the research scope introduced in <xref>[12]</xref>. Specifically, we determine the minimum Byzantine effort required to manipulate the sample distribution in the testing stage to given Byzantine sample distributions. Then, we derived the optimal and near-optimal Byzantine sample distributions subject to a fixed compromising effort. Additionally, a closed-form expression of optimal weights for FL is obtained, via which a connection between the optimal weights and those obtained from the FL training can be established. Through numerical experiments, we confirm the effectiveness of the proposed probabilistic Byzantine attack, which can serve as a good test to anti-attack defense strategies.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1823-1838"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LEMUR: Latent EM Unsupervised Regression for Sparse Inverse Problems","authors":"Pierre Barbault;Matthieu Kowalski;Charles Soussen","doi":"10.1109/TSP.2025.3565018","DOIUrl":"10.1109/TSP.2025.3565018","url":null,"abstract":"Most methods for sparse signal recovery require setting one or several hyperparameters. We propose an unsupervised method to estimate the parameters of a Bernoulli-Gaussian (BG) model describing sparse signals. The proposed method is first derived for denoising problems using a maximum likelihood (ML) approach. Then, an extension to general inverse problems is achieved through a latent variable formulation. Two expectation-maximization (EM) algorithms are then proposed to estimate the signal together with the BG model parameters. Combining these two approaches leads to the proposed LEMUR algorithm. LEMUR is then evaluated on extensive simulations regarding the ability to recover the parameters and provide accurate sparse signal estimates.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2087-2098"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential Error Feedback for Communication-Efficient Decentralized Learning","authors":"Roula Nassif;Stefan Vlaski;Marco Carpentiero;Vincenzo Matta;Ali H. Sayed","doi":"10.1109/TSP.2025.3564416","DOIUrl":"10.1109/TSP.2025.3564416","url":null,"abstract":"Communication-constrained algorithms for decentralized learning and optimization rely on local updates coupled with the exchange of compressed signals. In this context, <italic>differential quantization</i> is an effective technique to mitigate the negative impact of compression by leveraging correlations between successive iterates. In addition, the use of <italic>error feedback</i>, which consists of incorporating the compression error into subsequent steps, is a powerful mechanism to compensate for the bias caused by the compression. Under error feedback, performance guarantees in the literature have so far focused on algorithms employing a fusion center or a special class of contractive compressors that cannot be implemented with a finite number of bits. In this work, we propose a new <italic>decentralized</i> communication-efficient learning approach that blends differential quantization with error feedback. The approach is specifically tailored for decentralized learning problems where agents have individual risk functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus or single-task optimization as special cases, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. We show that, under some general conditions on the compression noise, and for sufficiently small step-sizes <inline-formula><tex-math>$mu$</tex-math></inline-formula>, the resulting communication-efficient strategy is stable both in terms of mean-square error and average bit rate: by reducing <inline-formula><tex-math>$mu$</tex-math></inline-formula>, it is possible to keep the <italic>estimation errors small (on the order of</i> <inline-formula><tex-math>$mu$</tex-math></inline-formula><italic>) without increasing indefinitely the bit rate as</i> <inline-formula><tex-math>$murightarrow 0$</tex-math></inline-formula>. The results establish that, in the <italic>small step-size regime</i> and with a <italic>finite number of bits</i>, it is possible to attain the performance achievable in the absence of compression.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1905-1921"},"PeriodicalIF":4.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality","authors":"Xilai Fan;Ya-Feng Liu;Liang Liu;Tsung-Hui Chang","doi":"10.1109/TSP.2025.3564126","DOIUrl":"10.1109/TSP.2025.3564126","url":null,"abstract":"This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via rate-limited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users’ signal-to-interference-plus-noise ratio (SINR) constraints and all BSs’ fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible; (2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2070-2086"},"PeriodicalIF":4.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetric Graph Error Control With Low Complexity in Causal Bandits","authors":"Chen Peng;Di Zhang;Urbashi Mitra","doi":"10.1109/TSP.2025.3564303","DOIUrl":"10.1109/TSP.2025.3564303","url":null,"abstract":"In this paper, the causal bandit problem is investigated, with the objective of maximizing the long-term reward by selecting an optimal sequence of interventions on nodes in an unknown causal graph. It is assumed that both the causal topology and the distribution of interventions are unknown. First, based on the difference between the two types of graph identification errors (false positives and negatives), a causal graph learning method is proposed. Numerical results suggest that this method has a much lower sample complexity relative to the prior art by learning <italic>sub-graphs</i>. However, we note that a sample complexity analysis for the new algorithm has not been undertaken, as of yet. Under the assumption of minimum-mean squared error weight estimation, a new uncertainty bound tailored to the causal bandit problem is derived. This uncertainty bound drives an upper confidence bound-based intervention selection to optimize the reward. Further, we consider a particular instance of non-stationary bandits wherein both the causal topology and interventional distributions can change. Our solution is the design of a sub-graph change detection mechanism that requires a modest number of samples. Numerical results compare the new methodology to existing schemes and show a substantial performance improvement in stationary and non-stationary settings. Averaged over 100 randomly generated causal bandits, the proposed scheme takes significantly fewer samples to learn the causal structure and achieves a reward gain of 85% compared to existing approaches.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1792-1807"},"PeriodicalIF":4.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint User Selection and Hybrid Precoder Design for Massive MIMO Systems","authors":"Hossein Vaezy;Steven D. Blostein","doi":"10.1109/TSP.2025.3562858","DOIUrl":"10.1109/TSP.2025.3562858","url":null,"abstract":"Massive multiple-input multiple-output (MIMO) systems are a cornerstone of modern wireless communication, enabling significant improvements in capacity and reliability. However, the joint optimization of user selection and hybrid precoder/decoder design remains challenging due to the complexity introduced by spatial correlation, noisy channel information, and the non-convex nature of the problem. This paper addresses these challenges by considering the downlink of multi-user massive MIMO systems. A noisy version of channel information with spatial correlation between antennas is assumed to be available at the transmitter, and an optimization problem is formulated for joint user selection and hybrid analog/digital precoder design. The total sum rate of the network is considered as a design metric that leads to non-convex and NP-hard mixed-integer optimization. To address the non-convexity, an iterative method is proposed which results in multiple simpler bounding and relaxed convex sub-problems with closed-form solutions for analog precoders/decoders, digital decoders, and user selection. As a by-product, the proposed algorithm also optimizes the number of selected users with perfect or imperfect channel state information (CSI). A generalized user selection metric is also derived for massive MIMO systems with multiple-antenna users under both perfect and imperfect CSI, and is further analyzed for specific scenarios such as ZF, MRT, block diagonalized precoders, and large-scale MIMO settings. Finally, the method is extended to finite-resolution phase shifters and assessed for Rayleigh fading channels. The simulation results show that the proposed method performs favorably compared to other recent joint user selection and precoder designs.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1808-1822"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Adaptive $tau$-Lasso: Robustness and Oracle Properties","authors":"Emadaldin Mozafari-Majd;Visa Koivunen","doi":"10.1109/TSP.2025.3563225","DOIUrl":"10.1109/TSP.2025.3563225","url":null,"abstract":"This paper introduces a new regularized version of the robust <inline-formula><tex-math>$tau$</tex-math></inline-formula>-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates (explanatory variables). The resulting estimator, termed adaptive <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive <inline-formula><tex-math>$ell_{1}$</tex-math></inline-formula>-norm penalty term, which enables the selection of relevant variables and reduces the bias associated with large true regression coefficients. More specifically, this adaptive <inline-formula><tex-math>$ell_{1}$</tex-math></inline-formula>-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors <inline-formula><tex-math>$ p $</tex-math></inline-formula>, we show that the adaptive <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso has the oracle property, ensuring both variable-selection consistency and asymptotic normality under fairly mild conditions. Asymptotic normality applies only to the entries of the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We characterize its robustness by establishing the finite-sample breakdown point and the influence function. We carry out extensive simulations and observe that the class of <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings. We also validate our theoretical findings on robustness properties through simulations. In the face of outliers and high-leverage points, the adaptive <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso and <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso estimators achieve the best performance or match the best performances of competing regularized estimators, with minimal or no loss in terms of prediction and variable selection accuracy for almost all scenarios considered in this study. Therefore, the adaptive <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso and <inline-formula><tex-math>$tau$</tex-math></inline-formula>-Lasso estimators provide attractive tools for a variety of sparse linear regression problems, particularly in high-dimensional settings and when the data is contaminated by outliers and high-leverage points. However, it is worth noting that no particular estimator uniformly dominates others in all considered scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2464-2479"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}