IEEE Transactions on Signal Processing最新文献_第6页

Large-Scale Independent Vector Analysis (IVA-G) via Coresets 通过核集进行大规模独立向量分析（IVA-G）

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-13 DOI: 10.1109/TSP.2024.3517323

Ben Gabrielson;Hanlu Yang;Trung Vu;Vince Calhoun;Tülay Adali

{"title":"Large-Scale Independent Vector Analysis (IVA-G) via Coresets","authors":"Ben Gabrielson;Hanlu Yang;Trung Vu;Vince Calhoun;Tülay Adali","doi":"10.1109/TSP.2024.3517323","DOIUrl":"10.1109/TSP.2024.3517323","url":null,"abstract":"Joint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. “datasets”, into “sources” that are statistically dependent across datasets and independent within datasets. Despite this usefulness for analyzing multiple datasets, JBSS methods suffer from considerable computational costs and are typically intractable for hundreds or thousands of datasets. To address this issue, we present a methodology for how a subset of the datasets can be used to perform efficient JBSS over the full set. We motivate two such methods: a numerical extension of independent vector analysis (IVA) with the multivariate Gaussian model (IVA-G), and a recently proposed analytic method resembling generalized joint diagonalization (GJD). We derive nonidentifiability conditions for both methods, and then demonstrate how one can significantly improve these methods’ generalizability by an efficient representative subset selection method. This involves selecting a \u0000<italic>coreset</i>\u0000 (a weighted subset) that minimizes a measure of discrepancy between the statistics of the coreset and the full set. Using simulated and real functional magnetic resonance imaging (fMRI) data, we demonstrate significant scalability and source separation advantages of our “coreIVA-G” method vs. other JBSS methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"230-244"},"PeriodicalIF":4.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820906","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

Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing With a Shared Wireless Backhaul 基于共享无线回程的协同感知联合节点选择与资源分配优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-13 DOI: 10.1109/TSP.2024.3516709

Mingxin Chen;Ming-Min Zhao;An Liu;Min Li;Qingjiang Shi

{"title":"Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing With a Shared Wireless Backhaul","authors":"Mingxin Chen;Ming-Min Zhao;An Liu;Min Li;Qingjiang Shi","doi":"10.1109/TSP.2024.3516709","DOIUrl":"10.1109/TSP.2024.3516709","url":null,"abstract":"In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"67-82"},"PeriodicalIF":4.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820907","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

Near-Optimal MIMO Detection Using Gradient-Based MCMC in Discrete Spaces 离散空间中基于梯度MCMC的近最优MIMO检测

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-12 DOI: 10.1109/TSP.2024.3516502

Xingyu Zhou;Le Liang;Jing Zhang;Chao-Kai Wen;Shi Jin

{"title":"Near-Optimal MIMO Detection Using Gradient-Based MCMC in Discrete Spaces","authors":"Xingyu Zhou;Le Liang;Jing Zhang;Chao-Kai Wen;Shi Jin","doi":"10.1109/TSP.2024.3516502","DOIUrl":"10.1109/TSP.2024.3516502","url":null,"abstract":"The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of antennas. Recently, the combination of two powerful machine learning methods, Markov chain Monte Carlo (MCMC) sampling and gradient descent, has emerged as a highly efficient solution to address this issue. However, existing gradient-based MCMC detectors are heuristically designed and thus are theoretically untenable. To bridge this gap, we introduce a novel sampling algorithm tailored for discrete spaces. This algorithm leverages gradients from the underlying continuous spaces for acceleration while maintaining the validity of probabilistic sampling. We prove the convergence of this method and also analyze its convergence rate using both MCMC theory and empirical diagnostics. On this basis, we develop a MIMO detector that precisely samples from the target discrete distribution and generates posterior Bayesian estimates using these samples, whose performance is thereby theoretically guaranteed. Furthermore, our proposed detector is highly parallelizable and scalable to large MIMO dimensions, positioning it as a compelling candidate for next-generation wireless networks. Simulation results show that our detector achieves near-optimal performance, significantly outperforms state-of-the-art baselines, and showcases resilience to various system setups.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"584-600"},"PeriodicalIF":4.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815710","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

Diffusion Stochastic Optimization for Min-Max Problems 最小-最大问题的扩散随机优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-12 DOI: 10.1109/TSP.2024.3511452

Haoyuan Cai;Sulaiman A. Alghunaim;Ali H. Sayed

{"title":"Diffusion Stochastic Optimization for Min-Max Problems","authors":"Haoyuan Cai;Sulaiman A. Alghunaim;Ali H. Sayed","doi":"10.1109/TSP.2024.3511452","DOIUrl":"10.1109/TSP.2024.3511452","url":null,"abstract":"The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of \u0000<inline-formula><tex-math>$mathcal{O}(varepsilon^{-2})$</tex-math></inline-formula>\u0000 to achieve an \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-stationary solution, we introduce and analyze a new formulation termed Diffusion Stochastic Same-Sample Optimistic Gradient (DSS-OG). We prove its convergence and resolve the large batch issue by establishing a tighter upper bound, under the more general setting of nonconvex Polyak-Lojasiewicz (PL) risk functions. We also extend the applicability of the proposed method to the distributed scenario, where agents communicate with their neighbors via a left-stochastic protocol. To implement DSS-OG, we can query the stochastic gradient oracles in parallel with some extra memory overhead, resulting in a complexity comparable to its conventional counterpart. To demonstrate the efficacy of the proposed algorithm, we conduct tests by training generative adversarial networks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"259-274"},"PeriodicalIF":4.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815498","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

Wideband Sensor Resource Allocation for Extended Target Tracking and Classification 面向扩展目标跟踪与分类的宽带传感器资源分配

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-12 DOI: 10.1109/TSP.2024.3512615

Hao Jiao;Junkun Yan;Wenqiang Pu;Yijun Chen;Hongwei Liu;Maria Sabrina Greco

{"title":"Wideband Sensor Resource Allocation for Extended Target Tracking and Classification","authors":"Hao Jiao;Junkun Yan;Wenqiang Pu;Yijun Chen;Hongwei Liu;Maria Sabrina Greco","doi":"10.1109/TSP.2024.3512615","DOIUrl":"10.1109/TSP.2024.3512615","url":null,"abstract":"Communication base stations can achieve high-precision tracking and accurate classification for multiple extended targets in the context of integrated communication and sensing by transmitting wideband signal. However, the time resources of the base stations are often limited. In the time-division operation mode, part of the time resources must be reserved to guarantee communication performance, while the rest of the resources must be properly allocated for better multi-target sensing performance. To deal with this, we develop a sensing task-oriented resource allocation (RA) scheme for wideband sensors. We first derive the Cramér–Rao lower bound for the estimation errors of position and shape parameters of the extended targets, and analyze their inside relations w.r.t. the resource vectors. Based on this, we construct the evaluation metric of tracking and classification performance, and subsequently build a non-smooth mathematical resource optimization model to maximize the target capacity within predetermined tracking and classification requirements. To solve this RA model, we then design an efficient two-step solution technique that incorporates dual transformation and discrete search. Finally, simulation results demonstrate that the proposed RA scheme can greatly increase the number of the well sensed targets within a limited sensing resource budget.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"55-66"},"PeriodicalIF":4.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815712","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

Learning State-Augmented Policies for Information Routing in Communication Networks 学习通信网络中信息路由的状态增强策略

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-12 DOI: 10.1109/TSP.2024.3516556

Sourajit Das;Navid NaderiAlizadeh;Alejandro Ribeiro

引用次数: 0

GAN Training With Kernel Discriminators: What Parameters Control Convergence Rates? 用核鉴别器训练GAN：什么参数控制收敛速率？

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-12 DOI: 10.1109/TSP.2024.3516083

Evan Becker;Parthe Pandit;Sundeep Rangan;Alyson K. Fletcher

引用次数: 0

Wideband Beamforming With RIS: A Unified Framework via Space-Frequency Transformation 基于RIS的宽带波束形成：基于空频变换的统一框架

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-11 DOI: 10.1109/TSP.2024.3515102

Xiaowei Qian;Xiaoling Hu;Chenxi Liu;Mugen Peng

{"title":"Wideband Beamforming With RIS: A Unified Framework via Space-Frequency Transformation","authors":"Xiaowei Qian;Xiaoling Hu;Chenxi Liu;Mugen Peng","doi":"10.1109/TSP.2024.3515102","DOIUrl":"10.1109/TSP.2024.3515102","url":null,"abstract":"The spectrum shift from sub-6G bands to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that the RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target with the aid of the RIS. In the proposed design, we exploit space-frequency Fourier transformation and stationary phase method to derive an approximate closed-form solution of RIS phase shifts, which significantly reduces the signal processing complexity compared to existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performance in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"173-187"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804785","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

A Dual Inexact Nonsmooth Newton Method for Distributed Optimization 分布式优化的双重非精确非光滑牛顿法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-11 DOI: 10.1109/TSP.2024.3514676

Dunbiao Niu;Yiguang Hong;Enbin Song

{"title":"A Dual Inexact Nonsmooth Newton Method for Distributed Optimization","authors":"Dunbiao Niu;Yiguang Hong;Enbin Song","doi":"10.1109/TSP.2024.3514676","DOIUrl":"10.1109/TSP.2024.3514676","url":null,"abstract":"In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"188-203"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142805228","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

A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks 基于统一优化的可证鲁棒公平图神经网络框架

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-11 DOI: 10.1109/TSP.2024.3514091

Vipul Kumar Singh;Sandeep Kumar;Avadhesh Prasad;Jayadeva

{"title":"A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks","authors":"Vipul Kumar Singh;Sandeep Kumar;Avadhesh Prasad;Jayadeva","doi":"10.1109/TSP.2024.3514091","DOIUrl":"https://doi.org/10.1109/TSP.2024.3514091","url":null,"abstract":"Graph Neural Networks (GNNs) have exhibited exceptional performance across diverse application domains by harnessing the inherent interconnectedness of data. Recent findings point towards instability of GNN under both feature and structure perturbations. The emergence of adversarial attacks targeting GNNs poses a substantial and pervasive threat, compromising their overall performance and learning capabilities. In this work, we first derive a theoretical bound on the global Lipschitz constant of GNN in the context of both feature and structure perturbations. Consequently, we propose a unifying approach, termed AdaLipGNN, for adversarial training of GNNs through an optimization framework which provides attack agnostic robustness. By seamlessly integrating graph denoising and network regularization, AdaLipGNN offers a comprehensive and versatile solution, extending its applicability and enabling robust regularization for diverse network architectures. Further, we develop a provably convergent iterative algorithm, leveraging block successive upper-bound minimization to learn robust and stable GNN hypothesis. Numerical results obtained from extensive experiments performed on real-world datasets clearly illustrate that the proposed AdaLipGNN outperforms other defence methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"83-98"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905960","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