IEEE Transactions on Signal Processing最新文献

{"title":"A Graph Signal Processing Perspective of Network Multiple Hypothesis Testing with False Discovery Rate Control","authors":"Xingchao Jian, Martin Gölz, Feng Ji, Wee Peng Tay, Abdelhak M. Zoubir","doi":"10.1109/tsp.2025.3593659","DOIUrl":"https://doi.org/10.1109/tsp.2025.3593659","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"15 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737086","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":"Achievable Rate Maximization in MIMO-BICM Systems: A Unified Transceiver Design","authors":"Jie Yang;Wanchen Hu;Yi Jiang;Shuangyang Li;Xin Wang","doi":"10.1109/TSP.2025.3592835","DOIUrl":"10.1109/TSP.2025.3592835","url":null,"abstract":"Transceiver designs for multiple-input multiple-output (MIMO) systems have been extensively studied in the past decades. However, in the context of finite constellation inputs, existing transceiver designs do not guarantee optimal performance across all SNR ranges or varying code rates. In this paper, we propose a novel maximum achievable rate transceiver (MART) design for bit-interleaved coded modulation in MIMO (MIMO-BICM) systems. Our proposed MART scheme combines channel matrix decomposition and decision feedback equalization (DFE), decomposing the MIMO channel into multiple parallel subchannels. We establish a tight lower bound for achievable rates in MIMO-BICM, based on which we transform the sophisticated problem to the optimization of the output signal-to-noise ratios (SNRs) of subchannels. We consider the achievable rate maximization under both zero-forcing (ZF) and minimum mean-square error (MMSE) criteria and provide a unified solution framework for both two problems. To simplify the optimization process, we further propose an alternative near-optimal scheme for both ZF and MMSE problems. Numerical results show the optimality of the proposed MART scheme across all SNR ranges, demonstrating its potential in practical MIMO-BICM systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3346-3361"},"PeriodicalIF":5.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736920","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":"Step Size Adaptation for Accelerated Stochastic Momentum Algorithm Using SDE Modeling and Lyapunov Drift Minimization","authors":"Yulan Yuan;Danny H. K. Tsang;Vincent K. N. Lau","doi":"10.1109/TSP.2025.3592678","DOIUrl":"10.1109/TSP.2025.3592678","url":null,"abstract":"Training machine learning models often involves solving high-dimensional stochastic optimization problems, where stochastic gradient-based algorithms are hindered by slow convergence. Although momentum-based methods perform well in deterministic settings, their effectiveness diminishes under gradient noise. In this paper, we introduce a novel accelerated stochastic momentum algorithm. Specifically, we first model the trajectory of discrete-time momentum-based algorithms using continuous-time stochastic differential equations (SDEs). By leveraging a tailored Lyapunov function, we derive 2-D adaptive step sizes through Lyapunov drift minimization, which significantly enhance both convergence speed and noise stability. The proposed algorithm not only accelerates convergence but also eliminates the need for hyperparameter fine-tuning, consistently achieving robust accuracy in machine learning tasks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3124-3139"},"PeriodicalIF":5.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712144","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":"Rational Gaussian Wavelets and Corresponding Model Driven Neural Networks","authors":"Attila Miklós Ámon;Kristian Fenech;Péter Kovács;Tamás Dózsa","doi":"10.1109/TSP.2025.3592099","DOIUrl":"10.1109/TSP.2025.3592099","url":null,"abstract":"In this paper we introduce a highly adaptive continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a number of numerical experiments including biomedical applications, and the detection of abnormal road surface based on tire sensor signals.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3140-3155"},"PeriodicalIF":5.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702050","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}

{"title":"Analyses of Successful Sparse Signal Recovery via Tail-$ell_{2}$ Minimization","authors":"Menglin Ye;Shidong Li;Cheng Cheng;Jun Xian","doi":"10.1109/TSP.2025.3588294","DOIUrl":"10.1109/TSP.2025.3588294","url":null,"abstract":"Recovery guarantee analyses of sparse signals by the tail-<inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> minimization approach are presented. Known for the lack of sparse recovery capacity by traditional <inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> minimization, its variation by an iterative tail-<inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> penalty procedure, however, is shown to be exceedingly effective in sparse selections. The analytical close-form solutions of the tail-<inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> formulation also reveal its superb efficiency. This article is focused on the analyses of the successful recovery by the tail-<inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> technique. A necessary and sufficient condition for the uniqueness of the tail-<inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> minimizer is established, which is seen inherently different from that of a similar tail-<inline-formula><tex-math>$ell_{1}$</tex-math></inline-formula> minimization problem. The inherent differences lead to further analyses of sufficient conditions for the uniqueness, and a notion of admissible solutions. Successful probability analysis is then carried out based on these conditions. The estimated probability of successful recovery <inline-formula><tex-math>$mathbb{P}_{T}$</tex-math></inline-formula> is righteously related to the cardinality of <inline-formula><tex-math>$T^{c}cap S$</tex-math></inline-formula>, where <inline-formula><tex-math>$T$</tex-math></inline-formula> is an estimated support of the solution index <inline-formula><tex-math>$S$</tex-math></inline-formula>. The smaller the <inline-formula><tex-math>$|T^{c}cap S|$</tex-math></inline-formula> is, the greater the <inline-formula><tex-math>$mathbb{P}_{T}$</tex-math></inline-formula> will be, and <inline-formula><tex-math>$mathbb{P}_{T}$</tex-math></inline-formula> naturally approaches 1 as <inline-formula><tex-math>$|T^{c}cap S|$</tex-math></inline-formula> approaches 0. Numerical experiments sufficiently validate the efficiency and the successful probability of the sparse signal recovery by the tail-<inline-formula><tex-math>$ell_{2}$</tex-math></inline-formula> minimization procedure.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2928-2939"},"PeriodicalIF":5.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694039","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":"Antenna Position and Beamforming Optimization for Movable Antenna Enabled ISAC: Optimal Solutions and Efficient Algorithms","authors":"Lebin Chen, Ming-Min Zhao, Min-Jian Zhao, Rui Zhang","doi":"10.1109/tsp.2025.3590379","DOIUrl":"https://doi.org/10.1109/tsp.2025.3590379","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"115 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684554","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":"Shuffled Linear Regression via Spectral Matching","authors":"Hang Liu;Anna Scaglione","doi":"10.1109/TSP.2025.3590466","DOIUrl":"10.1109/TSP.2025.3590466","url":null,"abstract":"Shuffled linear regression (SLR) seeks to estimate latent features through a linear transformation, complicated by unknown permutations in the measurement dimensions. This problem extends traditional least-squares (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) approaches by jointly estimating the permutation, resulting in shuffled LS and shuffled LASSO formulations. Existing methods, constrained by the combinatorial complexity of permutation recovery, often address small-scale cases with limited measurements. In contrast, we focus on large-scale SLR, particularly suited for environments with abundant measurement samples. We propose a spectral matching method that efficiently resolves permutations by aligning spectral components of the measurement and feature covariances. Rigorous theoretical analyses demonstrate that our method achieves accurate estimates in both shuffled LS and shuffled LASSO settings, given a sufficient number of samples. Furthermore, we extend our approach to address simultaneous pose and correspondence estimation in image registration tasks. Experiments on synthetic datasets and real-world image registration scenarios show that our method outperforms existing algorithms in both estimation accuracy and registration performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3014-3028"},"PeriodicalIF":5.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677242","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":"He2Cl: a 2-step clustering algorithm to characterize cellular heterogeneity from cell morpho-dynamics behaviors","authors":"Alice Martin, Guillaume Godefroy, Florian Lemarchand, Jeremy Neri, Olivier Cioni, Kiran Padmanabhan, Chiara Paviolo","doi":"10.1109/tsp.2025.3590564","DOIUrl":"https://doi.org/10.1109/tsp.2025.3590564","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"667 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684555","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":"Robust Two-Tier Beamforming for Distributed Signal Sensing","authors":"Zhixing Chen, Wenqiang Pu, Licheng Zhao, Qingjiang Shi","doi":"10.1109/tsp.2025.3590769","DOIUrl":"https://doi.org/10.1109/tsp.2025.3590769","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677276","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":"Optimal Symbol-Length Filter Design for Sidelobe Suppression in Filter Bank Based Orthogonal Time Frequency Space (FB-OTFS) Systems","authors":"Da Chen;Yixuan Zhan;Yuting Chen;Kai Luo;Wei Peng;Wei Wang","doi":"10.1109/TSP.2025.3590021","DOIUrl":"https://doi.org/10.1109/TSP.2025.3590021","url":null,"abstract":"In this paper, we propose symbol-length transceive filter optimization methods for sidelobe suppression in filter bank based orthogonal time frequency space (FB-OTFS) systems. Specifically, we firstly establish the FB-OTFS system model with fast implementation for transceive filters. Then, we analyze the impact of the transceive filters on the orthogonal transmission and derive the constraints for symbol-length transceive filters to achieve the orthogonal transmission. Moreover, the complexity analysis is provided. With the derived orthogonal conditions as constraints, we formulate a transceive filter optimization problem to minimize the stopband energy (a commonly used sidelobe suppression criterion), and derive the theoretically optimal solutions. To further achieve flexible suppression of the spectral sidelobes within specific frequency intervals, we formulate a transceive filter optimization to minimize the weighted stopband energy by designing adjustable frequency domain weights, and also obtain the optimal solutions. Numerical results demonstrate that: 1) The proposed transceive filters have the lowest spectral sidelobes compared with the commonly used rectangular pulse and the Gaussian filter; 2) The sidelobe suppression effects within specific frequency intervals are successfully controlled by designing the frequency domain weights; 3) All proposed transceive filters are verified to satisfy the orthogonal conditions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3094-3106"},"PeriodicalIF":5.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853161","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}