IEEE Transactions on Signal Processing最新文献_第3页

A Decentralized Primal-Dual Method With Quasi-Newton Tracking

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-04 DOI: 10.1109/TSP.2025.3547787

Liping Wang;Hao Wu;Hongchao Zhang

引用次数: 0

Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-04 DOI: 10.1109/tsp.2025.3546574

Siyuan Yu, Wei Chen, H. Vincent Poor

引用次数: 0

Generalized Quantum State Tomography With Hybrid Denoising Priors

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-28 DOI: 10.1109/tsp.2025.3546655

Duoduo Xue, Wenrui Dai, Ziyang Zheng, Chenglin Li, Junni Zou, Hongkai Xiong

引用次数: 0

Bayesian Two-Sample Hypothesis Testing Using the Uncertain Likelihood Ratio: Improving the Generalized Likelihood Ratio Test

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-28 DOI: 10.1109/TSP.2025.3546169

James Z. Hare;Yuchen Liang;Lance M. Kaplan;Venugopal V. Veeravalli

{"title":"Bayesian Two-Sample Hypothesis Testing Using the Uncertain Likelihood Ratio: Improving the Generalized Likelihood Ratio Test","authors":"James Z. Hare;Yuchen Liang;Lance M. Kaplan;Venugopal V. Veeravalli","doi":"10.1109/TSP.2025.3546169","DOIUrl":"10.1109/TSP.2025.3546169","url":null,"abstract":"Two-sample hypothesis testing is a common practice in many fields of science, where the goal is to identify whether a set of observations and a set of training data are drawn from the same distribution. Traditionally, this is achieved using parametric and non-parametric frequentist tests, such as the Generalized Likelihood Ratio (GLR) test. However, these tests are not optimal in a Neyman-Pearson sense, especially when the number of observations and training samples are finite. Therefore, in this work, we study a parametric Bayesian test, called the Uncertain Likelihood Ratio (ULR) test, and compare its performance to the traditional GLR test. We establish that the ULR test is the optimal test for any number of samples when the parameters of the likelihood models are drawn from the true prior distribution. We then study an asymptotic form of the ULR test statistic and compare it against the GLR test statistic. As a byproduct of this analysis, we establish a new asymptotic optimality property for the GLR test when the parameters of the likelihood models are drawn from the Jeffreys prior. Furthermore, we analyze conditions under which the ULR test outperforms the GLR test, and include a numerical study to validate the results.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1410-1425"},"PeriodicalIF":4.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526157","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

Communication-Efficient Distributed Bayesian Federated Learning Over Arbitrary Graphs

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-26 DOI: 10.1109/TSP.2025.3546328

Sihua Wang;Huayan Guo;Xu Zhu;Changchuan Yin;Vincent K. N. Lau

{"title":"Communication-Efficient Distributed Bayesian Federated Learning Over Arbitrary Graphs","authors":"Sihua Wang;Huayan Guo;Xu Zhu;Changchuan Yin;Vincent K. N. Lau","doi":"10.1109/TSP.2025.3546328","DOIUrl":"10.1109/TSP.2025.3546328","url":null,"abstract":"This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined in a communication graph to update the local model weights for minimizing the global loss function. To incorporate the communication graph constraint into the joint posterior distribution, we exploit the fact that the model weights on each device is a function of its local likelihood and local prior and then, the connectivity between adjacent devices is modeled by a Dirac Delta distribution. In this way, the joint distribution can be factorized naturally by a factor graph. Based on the Dirac Delta-based factor graph, we propose a novel distributed approximate Bayesian inference algorithm that combines loopy belief propagation (LBP) and variational Bayesian inference (VBI) for distributed FL. Specifically, VBI is used to approximate the non-Gaussian marginal posterior as a Gaussian distribution in local training process and then, the global training process resembles Gaussian LBP where only the mean and variance are passed among adjacent devices. Furthermore, we propose a new damping factor design according to the communication graph topology to mitigate the potential divergence and achieve consensus convergence. Simulation results verify that the proposed solution achieves faster convergence speed with better performance than baselines.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1351-1366"},"PeriodicalIF":4.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507268","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 Adaptive Spatial Filtering With Inexact Local Solvers

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-26 DOI: 10.1109/TSP.2025.3546484

Charles Hovine;Alexander Bertrand

引用次数: 0

Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-26 DOI: 10.1109/TSP.2025.3546458

Tal Vol;Loai Danial;Nir Shlezinger

{"title":"Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation","authors":"Tal Vol;Loai Danial;Nir Shlezinger","doi":"10.1109/TSP.2025.3546458","DOIUrl":"10.1109/TSP.2025.3546458","url":null,"abstract":"The ability to process signals in digital form depends on analog-to-digital converters (ADCs). Traditionally, ADCs are designed to ensure that the digital representation closely matches the analog signal. However, recent studies have shown that significant power and memory savings can be achieved through <italic>task-based acquisition</i>, where the acquisition process is tailored to the downstream processing task. An emerging technology for task-based acquisition involves the use of memristors, which are considered key enablers for neuromorphic computing. Memristors can implement ADCs with tunable mappings, allowing adaptation to specific system tasks or power constraints. In this work, we study task-based acquisition for a generic classification task using memristive ADCs. We consider the unique characteristics of this such neuromorphic ADCs, including their power consumption and noisy read-write behavior, and propose a physically compliant model based on resistive successive approximation register ADCs integrated with memristor components, enabling the adjustment of quantization regions. To optimize performance, we introduce a data-driven algorithm that jointly tunes task-based memristive ADCs alongside both digital and analog processing. Our design addresses the inherent stochasticity of memristors through power-aware distillation, complemented by a specialized learning algorithm that adapts to their unique analog-to-digital mapping. The proposed approach is shown to enhance accuracy by up to 27% and reduce power consumption by up to 66% compared to uniform ADCs. Even under noisy conditions, our method achieves substantial gains, with accuracy improvements of up to 19% and power reductions of up to 57%. These results highlight the effectiveness of our power-aware neuromorphic ADCs in improving system performance across diverse tasks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1246-1261"},"PeriodicalIF":4.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507278","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

Event-Triggered State Estimation Through Confidence Level

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-25 DOI: 10.1109/TSP.2025.3543721

Wei Liu

引用次数: 0

Relative Entropy Based Jamming Signal Design Against Radar Target Detection

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-25 DOI: 10.1109/TSP.2025.3544305

Zhou Xu;Bo Tang;Weihua Ai;Jiahua Zhu

引用次数: 0

Reinforcement Learning Based Online Algorithm for Near-Field Time-Varying IRS Phase Shift Optimization: System Evolution Perspective

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-24 DOI: 10.1109/TSP.2025.3545164

Zongtai Li;Rui Wang;Erwu Liu

{"title":"Reinforcement Learning Based Online Algorithm for Near-Field Time-Varying IRS Phase Shift Optimization: System Evolution Perspective","authors":"Zongtai Li;Rui Wang;Erwu Liu","doi":"10.1109/TSP.2025.3545164","DOIUrl":"10.1109/TSP.2025.3545164","url":null,"abstract":"This paper proposes a reinforcement learning (RL) based intelligent reflecting surface (IRS) incremental control algorithm for a mmWave time-varying multi-user multiple-input single-output (MU-MISO) system. The research focuses on addressing the key challenge of near-field IRS design, which involves time-varying channels due to users’ mobility. In practice, the optimization becomes more challenging when the components of the concatenated channel are unknown. From a higher perspective, we leverage electromagnetic information theory and manifold theory to provide a unified description of the IRS-assisted MU-MISO system. We regard the communication system as a nonlinear dynamic system on reproducing kernel Hilbert space (RKHS), upon which the approximate evolution operator is defined as observables for system evolution. The IRS phase shift optimization problem is modeled as a nonlinear system eigenvalue maximization problem. Utilizing the geometric properties of the unitary evolution operator, we define a metric space where the geodesic-based distance function satisfies the Lipschitz condition, enabling efficient exploitation of channel similarities. We transform the complex non-convex optimization problem into a low-dimensional linear contextual bandit problem. The performance of the proposed GLinUCB algorithm is evaluated through numerical simulations in various scenarios, showing its effectiveness in achieving high sum rates with fast convergence speed.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1231-1245"},"PeriodicalIF":4.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486209","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