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IEEE Transactions on Signal Processing最新文献

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The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-24 DOI: 10.1109/tsp.2025.3533208
Tongle Wu, Zhize Li, Ying Sun
{"title":"The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity","authors":"Tongle Wu, Zhize Li, Ying Sun","doi":"10.1109/tsp.2025.3533208","DOIUrl":"https://doi.org/10.1109/tsp.2025.3533208","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"58 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030952","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}
引用次数: 0
Wasserstein Distributionally Robust Graph Learning via Algorithm Unrolling
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-23 DOI: 10.1109/tsp.2025.3526287
Xiang Zhang, Yinfei Xu, Mingjie Shao, Yonina C. Eldar
{"title":"Wasserstein Distributionally Robust Graph Learning via Algorithm Unrolling","authors":"Xiang Zhang, Yinfei Xu, Mingjie Shao, Yonina C. Eldar","doi":"10.1109/tsp.2025.3526287","DOIUrl":"https://doi.org/10.1109/tsp.2025.3526287","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"110 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026443","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}
引用次数: 0
On Optimal MMSE Channel Estimation for One-Bit Quantized MIMO Systems 论一位量化多输入多输出系统的最优 MMSE 信道估计
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-21 DOI: 10.1109/TSP.2025.3531779
Minhua Ding;Italo Atzeni;Antti Tölli;A. Lee Swindlehurst
{"title":"On Optimal MMSE Channel Estimation for One-Bit Quantized MIMO Systems","authors":"Minhua Ding;Italo Atzeni;Antti Tölli;A. Lee Swindlehurst","doi":"10.1109/TSP.2025.3531779","DOIUrl":"10.1109/TSP.2025.3531779","url":null,"abstract":"This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE estimator has not been fully investigated in this context due to its general nonlinearity and computational complexity. Instead, the typically suboptimal Bussgang linear MMSE (BLMMSE) channel estimator has been widely adopted. In this work, we develop a new framework to compute the MMSE channel estimator that hinges on the computation of the orthant probability of a multivariate normal distribution. Based on this framework, we determine a necessary and sufficient condition for the BLMMSE channel estimator to be optimal and thus equivalent to the MMSE estimator. Under the assumption of specific channel correlation or pilot symbols, we further utilize the framework to derive analytical expressions for the MMSE estimator that are particularly convenient for the computation when certain system dimensions become large, thereby enabling a comparison between the BLMMSE and MMSE channel estimators in these cases.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"617-632"},"PeriodicalIF":4.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10848316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dynamic Spectrum Cartography: Reconstructing Spatial-Spectral-Temporal Radio Frequency Map via Tensor Completion 动态频谱制图：通过张量补全重构空间-光谱-时间无线电频率图
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-21 DOI: 10.1109/tsp.2025.3531872
Xiaonan Chen, Jun Wang, Qingyang Huang
{"title":"Dynamic Spectrum Cartography: Reconstructing Spatial-Spectral-Temporal Radio Frequency Map via Tensor Completion","authors":"Xiaonan Chen, Jun Wang, Qingyang Huang","doi":"10.1109/tsp.2025.3531872","DOIUrl":"https://doi.org/10.1109/tsp.2025.3531872","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"82 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992785","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}
引用次数: 0
Single-Source Localization as an Eigenvalue Problem 单源定位作为特征值问题
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-21 DOI: 10.1109/TSP.2025.3532102
Martin Larsson;Viktor Larsson;Kalle Åström;Magnus Oskarsson
{"title":"Single-Source Localization as an Eigenvalue Problem","authors":"Martin Larsson;Viktor Larsson;Kalle Åström;Magnus Oskarsson","doi":"10.1109/TSP.2025.3532102","DOIUrl":"10.1109/TSP.2025.3532102","url":null,"abstract":"This paper introduces a novel method for solving the single-source localization problem, specifically addressing the case of trilateration. We formulate the problem as a weighted least-squares problem in the squared distances and demonstrate how suitable weights are chosen to accommodate different noise distributions. By transforming this formulation into an eigenvalue problem, we leverage existing eigensolvers to achieve a fast, numerically stable, and easily implemented solver. Furthermore, our theoretical analysis establishes that the globally optimal solution corresponds to the largest real eigenvalue, drawing parallels to the existing literature on the trust-region subproblem. Unlike previous works, we give special treatment to degenerate cases, where multiple and possibly infinitely many solutions exist. We provide a geometric interpretation of the solution sets and design the proposed method to handle these cases gracefully. Finally, we validate against a range of state-of-the-art methods using synthetic and real data, demonstrating how the proposed method is among the fastest and most numerically stable.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"574-583"},"PeriodicalIF":4.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992786","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}
引用次数: 0
On the Convergence of Decentralized Stochastic Gradient Descent With Biased Gradients 论有偏梯度的分散随机梯度下降的收敛性
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-20 DOI: 10.1109/TSP.2025.3531356
Yiming Jiang;Helei Kang;Jinlan Liu;Dongpo Xu
{"title":"On the Convergence of Decentralized Stochastic Gradient Descent With Biased Gradients","authors":"Yiming Jiang;Helei Kang;Jinlan Liu;Dongpo Xu","doi":"10.1109/TSP.2025.3531356","DOIUrl":"10.1109/TSP.2025.3531356","url":null,"abstract":"Stochastic optimization algorithms are widely used to solve large-scale machine learning problems. However, their theoretical analysis necessitates access to unbiased estimates of the true gradients. To address this issue, we perform a comprehensive convergence rate analysis of stochastic gradient descent (SGD) with biased gradients for decentralized optimization. In non-convex settings, we show that for decentralized SGD utilizing biased gradients, the gradient in expectation is bounded asymptotically at a rate of <inline-formula><tex-math>$mathcal{O}(1/sqrt{nT}+n/T)$</tex-math></inline-formula>, and the bound is linearly correlated to the biased gradient gap. In particular, we can recover the convergence results in the unbiased stochastic gradient setting when the biased gradient gap is zero. Lastly, we provide empirical support for our theoretical findings through extensive numerical experiments.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"549-558"},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991538","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}
引用次数: 0
Distributed Center-based Clustering: A Unified Framework 基于中心的分布式集群：统一框架
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-20 DOI: 10.1109/tsp.2025.3531292
Aleksandar Armacki, Dragana Bajović, Dušan Jakovetić, Soummya Kar
{"title":"Distributed Center-based Clustering: A Unified Framework","authors":"Aleksandar Armacki, Dragana Bajović, Dušan Jakovetić, Soummya Kar","doi":"10.1109/tsp.2025.3531292","DOIUrl":"https://doi.org/10.1109/tsp.2025.3531292","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991539","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}
引用次数: 0
Robust In-Memory Computation With Bayesian Analog Error Mitigating Codes 基于贝叶斯模拟错误缓解码的鲁棒内存计算
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-16 DOI: 10.1109/TSP.2025.3530149
Nilesh Kumar Jha;Huayan Guo;Vincent K. N. Lau
{"title":"Robust In-Memory Computation With Bayesian Analog Error Mitigating Codes","authors":"Nilesh Kumar Jha;Huayan Guo;Vincent K. N. Lau","doi":"10.1109/TSP.2025.3530149","DOIUrl":"10.1109/TSP.2025.3530149","url":null,"abstract":"In-memory computation (IMC) is a promising technology for enabling low-latency and energy-efficient deep learning and artificial intelligence (AI) applications at edge devices. However, the IMC crossbar array, typically implemented using resistive random access memory (RRAM), faces hardware defects that pose a significant challenge to reliable computation. This paper presents a robust IMC scheme utilizing Bayesian neural network-accelerated analog codes. Our approach includes a new datapath design comprising a parity matrix generator and a low-complexity decoder module to facilitate analog codes for IMC. Moreover, we introduce a Gaussian mixture model-based error prior to capture impulsive error statistics and leverage variational Bayesian inference (VBI) techniques for training neural network weights. Extensive simulations confirm the effectiveness of our proposed solution compared to various state-of-the-art baseline schemes.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"534-548"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987669","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}
引用次数: 0
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics 通过隐性八卦增强联邦学习：在未知和任意动态中减轻连接不可靠性
IF 5.4 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-16 DOI: 10.1109/tsp.2025.3526782
Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
{"title":"Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics","authors":"Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su","doi":"10.1109/tsp.2025.3526782","DOIUrl":"https://doi.org/10.1109/tsp.2025.3526782","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"49 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987243","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}
引用次数: 0
Optimal Beamforming for MIMO DFRC Systems With Transmit Covariance Constraints 具有发射协方差约束的MIMO DFRC系统的最优波束形成
IF 4.6 2区 工程技术
IEEE Transactions on Signal Processing Pub Date : 2025-01-15 DOI: 10.1109/TSP.2025.3529722
Chenhao Yang;Xin Wang;Wei Ni;Yi Jiang
{"title":"Optimal Beamforming for MIMO DFRC Systems With Transmit Covariance Constraints","authors":"Chenhao Yang;Xin Wang;Wei Ni;Yi Jiang","doi":"10.1109/TSP.2025.3529722","DOIUrl":"10.1109/TSP.2025.3529722","url":null,"abstract":"This paper optimizes the beamforming design of a downlink multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system to maximize the weighted communication sum-rate under a prescribed transmit covariance constraint for radar performance guarantee. In the single-user case, we show that the transmit covariance constraint implies the existence of inherent orthogonality among the transmit beamforming vectors in use. Then, leveraging Cauchy's interlace theorem, we derive the globally optimal transmit and receive beamforming solution in closed form. In the multi-user case, we exploit the connection between the weighted sum-rate and weighted minimum mean squared error (MMSE) to reformulate the intended problem, and develop a block-coordinate-descent (BCD) algorithm to iteratively compute the transmit beamforming and receive beamforming solutions. Under this approach, we reveal that the optimal receive beamforming is given by the classic MMSE one and the optimal transmit beamforming design amounts to solving an orthogonal Procrustes problem, thereby allowing for closed-form solutions to subproblems in each BCD step and fast convergence of the proposed algorithm to a high-quality (near-optimal) overall beamforming design. Numerical results demonstrate the superiority of our approach to the existing methods, with at least 40% higher sum-rate under a multi-user MIMO setting in the high signal-to-noise regime.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"601-616"},"PeriodicalIF":4.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986646","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}
引用次数: 0
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