IEEE Transactions on Signal Processing最新文献_第4页

Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-24 DOI: 10.1109/TSP.2025.3544517

Zhen Yang;Wangli He;Shaofu Yang

{"title":"Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates","authors":"Zhen Yang;Wangli He;Shaofu Yang","doi":"10.1109/TSP.2025.3544517","DOIUrl":"10.1109/TSP.2025.3544517","url":null,"abstract":"This paper addresses the distributed optimization problem with privacy concerns over time-varying unbalanced networks, where agents collaborate to optimize the average of local objective functions while preserving the privacy of sensitive information encoded in local functions. To tackle the problem, the paper proposes a differentially private algorithm by exploiting decaying Laplace noise without requiring bounded gradients. The proposed algorithm is demonstrated to achieve linear convergence to the sub-optimal solution determined by the noise injected to gradient estimations in mean square and ensure <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-differential privacy (DP) of local functions under carefully designed noise parameters. The inherent privacy-accuracy trade-off is revealed through both theoretical insights and simulation results. Furthermore, the image classification and deblurring problems are effectively solved with sensitive data being strictly protected through the deployment of the proposed algorithm, demonstrating the convergence and privacy-preserving performance of the algorithm.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1138-1152"},"PeriodicalIF":4.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486210","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

Identifying a Piecewise Affine Signal From Its Nonlinear Observation—Application to DNA Replication Analysis

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-21 DOI: 10.1109/TSP.2025.3544463

Clara Lage;Nelly Pustelnik;Jean-Michel Arbona;Benjamin Audit;Rémi Gribonval

{"title":"Identifying a Piecewise Affine Signal From Its Nonlinear Observation—Application to DNA Replication Analysis","authors":"Clara Lage;Nelly Pustelnik;Jean-Michel Arbona;Benjamin Audit;Rémi Gribonval","doi":"10.1109/TSP.2025.3544463","DOIUrl":"10.1109/TSP.2025.3544463","url":null,"abstract":"We consider a <italic>nonlinear</i> inverse problem where the unknown is assumed to be piecewise affine, which is motivated by an application in DNA replication analysis. Since traditional algorithmic and theoretical tools from linear inverse problems do not apply, we propose a novel formalism and computational approach to harness it. In the noiseless case, we establish sufficient identifiability conditions, and prove that the solution is the unique minimizer of a non-convex optimization problem. The latter is specially challenging because of its multiple local minima. We propose an optimization algorithm that provably finds the global solution in the noiseless case and is shown to be numerically effective for noisy signals. When instantiated in a DNA replication analysis scenario, where the unknown is a so-called timing profile, the approach is shown to be more computationally effective than the state-of-the-art optimization methods by at least 30 orders of magnitude. Besides, it automatically recovers the full configuration of the DNA replication dynamics, which is crucial for DNA replication analysis and was not possible with previous methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1278-1292"},"PeriodicalIF":4.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470801","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

Cognitive Radar Subpulses Waveform Design via Online Greedy Search

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-21 DOI: 10.1109/TSP.2025.3543868

Yi Wang;Xianxiang Yu;Jing Yang;Guolong Cui

{"title":"Cognitive Radar Subpulses Waveform Design via Online Greedy Search","authors":"Yi Wang;Xianxiang Yu;Jing Yang;Guolong Cui","doi":"10.1109/TSP.2025.3543868","DOIUrl":"10.1109/TSP.2025.3543868","url":null,"abstract":"This paper proposes a cognitive radar subpulse waveform design (CRSWD) approach including a smart acquirement process of some a prior information and multi-sequences optimization algorithm against multiple mainlobe interrupted sampling repeater jamming (ISRJ). Specifically, the radar first interacts with the environment on a pulse-by-pulse basis to quest for radar survival window (RSW) dynamically. Hence, a novel RSW searching method incorporating the greedy strategy is devised, where a reasonable value function is defined for measuring the anti-jamming and detection capabilities. Based on the estimated RSW information, orthogonal shielding and probing subpulses are strategically positioned for confusing the jammer and then detecting targets, respectively. To this respect, a non-convex optimization problem based on the peak to-sidelobe level (PSL) criterion and peak-to-average ratio (PAR) constraints is formulated for designing orthogonal probing and shielding waveforms with optimized RSW knowledge. A fast iterative methodology based on block coordinate descent (BCD) and majorize-minimization (MM) framework is proposed with the convergence performance ensured. Numerical simulations demonstrate that the proposed framework can effectively acquire jamming-resistant RSW in the presence of multiple targets and ISRJ with different parameters and achieve reliable detection outperforming some counterparts. Experiments are conducted to further verify its engineering feasibility.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1122-1137"},"PeriodicalIF":4.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470799","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

Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-21 DOI: 10.1109/TSP.2025.3544239

Gangle Sun;Mengyao Cao;Wenjin Wang;Wei Xu;Christoph Studer

{"title":"Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems","authors":"Gangle Sun;Mengyao Cao;Wenjin Wang;Wei Xu;Christoph Studer","doi":"10.1109/TSP.2025.3544239","DOIUrl":"10.1109/TSP.2025.3544239","url":null,"abstract":"Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1094-1109"},"PeriodicalIF":4.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470800","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

Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals 多项式图式套索：从高斯图式静态信号中学习边缘

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-21 DOI: 10.1109/TSP.2025.3544376

Andrei Buciulea;Jiaxi Ying;Antonio G. Marques;Daniel P. Palomar

{"title":"Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals","authors":"Andrei Buciulea;Jiaxi Ying;Antonio G. Marques;Daniel P. Palomar","doi":"10.1109/TSP.2025.3544376","DOIUrl":"10.1109/TSP.2025.3544376","url":null,"abstract":"This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph learning formulation that combines the strengths of graphical lasso with a more encompassing model. Specifically, we assume that the precision matrix can take any polynomial form of the sought graph, allowing for increased flexibility in modeling nodal relationships. Given the inherent complexity and nonconvexity of the optimization problem, we (i) propose a low-complexity algorithm that alternates between estimating the graph and precision matrices, and (ii) characterize its convergence. We evaluate the performance of PGL through comprehensive numerical simulations using both synthetic and real data, demonstrating its superiority over several alternatives. Overall, this approach presents a significant advancement in graph learning and holds promise for various applications in graph-aware signal analysis and beyond.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1153-1167"},"PeriodicalIF":4.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470803","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

Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources Than Sensors: A Deep Learning Methodology

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-21 DOI: 10.1109/TSP.2025.3544170

Kuan-Lin Chen;Bhaskar D. Rao

{"title":"Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources Than Sensors: A Deep Learning Methodology","authors":"Kuan-Lin Chen;Bhaskar D. Rao","doi":"10.1109/TSP.2025.3544170","DOIUrl":"10.1109/TSP.2025.3544170","url":null,"abstract":"Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize semidefinite programming (SDP). Although deep neural network (DNN)-based methods offer new alternatives, they still depend on covariance matrix fitting. In this paper, we develop a novel methodology that estimates the co-array subspaces from a sample covariance for SLAs. Our methodology trains a DNN to learn signal and noise subspace representations that are invariant to the selection of bases. To learn such representations, we propose loss functions that gauge the separation between the desired and the estimated subspace. In particular, we propose losses that measure the length of the shortest path between subspaces viewed on a union of Grassmannians, and prove that it is possible for a DNN to approximate signal subspaces. The computation of learning subspaces of different dimensions is accelerated by a new batch sampling strategy called consistent rank sampling. The methodology is robust to array imperfections due to its geometry-agnostic and data-driven nature. In addition, we propose a fully end-to-end gridless approach that directly learns angles to study the possibility of bypassing subspace methods. Numerical results show that learning such subspace representations is more beneficial than learning covariances or angles. It outperforms conventional SDP-based methods such as the sparse and parametric approach (SPA) and existing DNN-based covariance reconstruction methods for a wide range of signal-to-noise ratios (SNRs), snapshots, and source numbers for both perfect and imperfect arrays.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1293-1308"},"PeriodicalIF":4.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470804","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

Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-20 DOI: 10.1109/TSP.2025.3543753

Jaehyup Seong;Juha Park;Juhwan Lee;Jungwoo Lee;Jung-Bin Kim;Wonjae Shin;H. Vincent Poor

{"title":"Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications","authors":"Jaehyup Seong;Juha Park;Juhwan Lee;Jungwoo Lee;Jung-Bin Kim;Wonjae Shin;H. Vincent Poor","doi":"10.1109/TSP.2025.3543753","DOIUrl":"10.1109/TSP.2025.3543753","url":null,"abstract":"With the goal of ubiquitous global connectivity, multibeam low Earth orbit (LEO) satellite communications (SATCOM) has attracted significant attention in recent years. The traffic demands of users are heterogeneous within the broad coverage of SATCOM due to different geological conditions and user distributions. Motivated by this, this paper proposes a novel rate-matching (RM) framework based on rate-splitting multiple access (RSMA) that minimizes the difference between the traffic demands and offered rates while simultaneously minimizing transmit power for power-hungry satellite payloads. Moreover, channel phase perturbations arising from channel estimation and feedback errors are considered to capture realistic multibeam LEO SATCOM scenarios. To tackle the non-convexity of the RSMA-based RM problem under phase perturbations, we convert it into a tractable convex form via the successive convex approximation method and present an efficient algorithm to solve the RM problem. Through the extensive numerical analysis across various traffic demand distribution and channel state information accuracy at LEO satellites, we demonstrate that RSMA flexibly allocates the power between common and private streams according to different traffic patterns across beams, thereby efficiently satisfying users’ non-uniform traffic demands. In particular, the use of common messages plays a vital role in overcoming the limited spatial dimension available at LEO satellites, enabling it to manage inter-/intra-beam interference effectively in the presence of phase perturbation.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1426-1443"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462556","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

D2CN: Distributed Deep Convolutional Network

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-20 DOI: 10.1109/TSP.2025.3534177

Yi Ding;Junpeng Shi;Zai Yang;Zhiyuan Zhang;Yongxiang Liu;Xiang Li

{"title":"D2CN: Distributed Deep Convolutional Network","authors":"Yi Ding;Junpeng Shi;Zai Yang;Zhiyuan Zhang;Yongxiang Liu;Xiang Li","doi":"10.1109/TSP.2025.3534177","DOIUrl":"10.1109/TSP.2025.3534177","url":null,"abstract":"With the rapid growth of distributed systems, deep learning-based multi-source data processing has drawn extensive attention, especially for the multi-channel networks. However, the conventional ones lack a strong theoretical foundation and the data in each channel lack necessary interactions, giving rise to insufficient robustness. Here we derive a network termed as distributed deep convolutional network (D<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>CN) to overcome this issue, which is explained by integrating generalized singular value decomposition (GSVD) with the principles of Hankel convolution framelet. Specifically, we employ the feature extraction capability of GSVD to perform data interactions by forward/backward propagation, where numerous inputs are designed using the common bases and reliable performance is achieved by training a shared set of right bases. We go over the network's scalability to show its benefits in performance and robustness. Moreover, we show that the encoder-decoder scheme allows the network suitable for a wide range of inverse situations. Finally, we demonstrate the superiority of the D<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>CN over other fundamental networks through numerical experiments conducted on classical image denoising.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1309-1322"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462558","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

New Statistic Detector for Structural Image Similarity

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-18 DOI: 10.1109/TSP.2025.3543207

Moustapha Diaw;Florent Retraint;Frédéric Morain-Nicolier;Agnès Delahaies;Jérôme Landré

{"title":"New Statistic Detector for Structural Image Similarity","authors":"Moustapha Diaw;Florent Retraint;Frédéric Morain-Nicolier;Agnès Delahaies;Jérôme Landré","doi":"10.1109/TSP.2025.3543207","DOIUrl":"10.1109/TSP.2025.3543207","url":null,"abstract":"Social networks like LinkedIn, Facebook, and Instagram contribute significantly to the rise of image prevalence in daily life, with numerous images posted in everyday. Detecting image similarity is crucial for many applications. While deep learning methods like Learned Perceptual Image Patch Similarity (LPIPS) are popular, they often overlook image structure. An alternative method involves using pre-trained models (<inline-formula><tex-math>$e.g.$</tex-math></inline-formula>, LeNet-<inline-formula><tex-math>$5$</tex-math></inline-formula> and VGG-<inline-formula><tex-math>$16$</tex-math></inline-formula>) to extract features and employing classifiers. However, deep learning methods demand substantial computational resources and they also suffer from uncontrolled false alarms. This paper proposes a novel Generalized Likelihood Ratio Test (GLRT) detector based on a hypothesis testing framework to identify the similarity of structural image pairs. The proposed approach minimizes the need for extensive computational resources, and false alarms can be regulated by employing a threshold. The detector is applied to Local Dissimilarity Maps (LDM), with gray-level values modeled by a statistical distribution. Experimental results on simulated and real data confirm its effectiveness for structural similarity detection. Additionally, a Simple Likelihood Ratio Test (SLRT) is tested on simulated data. Comparisons with deep learning and classical measures like Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) show the proposed detector performs comparably or better in terms of Area Under the Curve (AUC) with less computing time, especially for structural similarity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1168-1183"},"PeriodicalIF":4.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443547","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

Approximating Multi-Dimensional and Multiband Signals

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-14 DOI: 10.1109/TSP.2025.3541872

Yuhan Li;Tianyao Huang;Lei Wang;Yimin Liu;Xiqin Wang

引用次数: 0