IEEE Transactions on Signal and Information Processing over Networks最新文献_第4页

Multi-Bit Distributed Detection of Sparse Stochastic Signals Over Error-Prone Reporting Channels 在易出错报告信道上对稀疏随机信号进行多比特分布式检测

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-20 DOI: 10.1109/TSIPN.2024.3496253

Linlin Mao;Shefeng Yan;Zeping Sui;Hongbin Li

{"title":"Multi-Bit Distributed Detection of Sparse Stochastic Signals Over Error-Prone Reporting Channels","authors":"Linlin Mao;Shefeng Yan;Zeping Sui;Hongbin Li","doi":"10.1109/TSIPN.2024.3496253","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3496253","url":null,"abstract":"We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic signals. To achieve a trade-off between detection performance and system constraints, multi-bit quantizers are employed at local sensors. Then, two quantization strategies, namely raw quantization (RQ) and likelihood ratio quantization (LQ), are examined. The multi-bit quantized signals undergo encoding into binary codewords and are subsequently transmitted to the fusion center via error-prone reporting channels. Upon exploiting the locally most powerful test (LMPT) strategy, we devise two multi-bit LMPT detectors in which quantized raw observations and local likelihood ratios are fused respectively. Moreover, the asymptotic detection performance of the proposed quantized detectors is analyzed, and closed-form expressions for the detection and false alarm probabilities are derived. Furthermore, the multi-bit quantizer design criterion, considering both RQ and LQ, is then proposed to achieve near-optimal asymptotic performance for our proposed detectors. The normalized Fisher information and asymptotic relative efficiency are derived, serving as tools to analyze and compensate for the loss of information introduced by the quantization. Simulation results validate the effectiveness of the proposed detectors, especially in scenarios with low signal-to-noise ratios and poor channel conditions.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"881-893"},"PeriodicalIF":3.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks 高阶GNNs满足效率：稀疏Sobolev图神经网络

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-20 DOI: 10.1109/TSIPN.2024.3503416

Jhony H. Giraldo;Aref Einizade;Andjela Todorovic;Jhon A. Castro-Correa;Mohsen Badiey;Thierry Bouwmans;Fragkiskos D. Malliaros

{"title":"Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks","authors":"Jhony H. Giraldo;Aref Einizade;Andjela Todorovic;Jhon A. Castro-Correa;Mohsen Badiey;Thierry Bouwmans;Fragkiskos D. Malliaros","doi":"10.1109/TSIPN.2024.3503416","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3503416","url":null,"abstract":"Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the information from higher-order neighbors in the graph, involving the incorporation of powers of the shift operator, such as the graph Laplacian or adjacency matrix. This approach comes with a trade-off in terms of increased computational and memory demands. Relying on graph spectral theory, we make a fundamental observation: \u0000<italic>the regular and the Hadamard power of the Laplacian matrix behave similarly in the spectrum</i>\u0000. This observation has significant implications for capturing higher-order information in GNNs for various tasks such as node classification and semi-supervised learning. Consequently, we propose a novel graph convolutional operator based on the sparse Sobolev norm of graph signals. Our approach, known as Sparse Sobolev GNN (S2-GNN), employs Hadamard products between matrices to maintain the sparsity level in graph representations. S2-GNN utilizes a cascade of filters with increasing Hadamard powers to generate a diverse set of functions. We theoretically analyze the stability of S2-GNN to show the robustness of the model against possible graph perturbations. We also conduct a comprehensive evaluation of S2-GNN across various graph mining, semi-supervised node classification, and computer vision tasks. In particular use cases, our algorithm demonstrates competitive performance compared to state-of-the-art GNNs in terms of performance and running time.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"11-22"},"PeriodicalIF":3.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probability-Guaranteed Distributed Filtering for Nonlinear Systems on Basis of Nonuniform Samplings Subject to Envelope Constraints 基于包络约束的非均匀采样的非线性系统概率保证分布式滤波

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-15 DOI: 10.1109/TSIPN.2024.3496254

Wei Wang;Chen Hu;Lifeng Ma;Xiaojian Yi

{"title":"Probability-Guaranteed Distributed Filtering for Nonlinear Systems on Basis of Nonuniform Samplings Subject to Envelope Constraints","authors":"Wei Wang;Chen Hu;Lifeng Ma;Xiaojian Yi","doi":"10.1109/TSIPN.2024.3496254","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3496254","url":null,"abstract":"This paper investigates the probability-guaranteed distributed \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 filtering problem for stochastic time-varying systems over sensor networks. The measurements from sensing nodes are sampled nonuniformly before being received by filters and the sampling processes are modeled by a set of Markov chains. The purpose of the addressed problem is to design a distributed filter algorithm which meets the finite-horizon average \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 performance, meanwhile guaranteeing all filtering errors bounded within a prespecified envelope with a certain probability. Sufficient conditions for the feasibility of the mentioned filtering technique are established using convex optimization techniques. The desired filtering gains are subsequently determined by resolving the relevant matrix inequalities at each time step. Finally, the effectiveness of the proposed filtering algorithm is shown via an illustrative numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"905-915"},"PeriodicalIF":3.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning With a Use-Case in Resource Allocation in Communication Networks 基于异步消息传递和零阶优化的分布式学习在通信网络资源分配中的应用

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-15 DOI: 10.1109/TSIPN.2024.3487421

Pourya Behmandpoor;Marc Moonen;Panagiotis Patrinos

{"title":"Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning With a Use-Case in Resource Allocation in Communication Networks","authors":"Pourya Behmandpoor;Marc Moonen;Panagiotis Patrinos","doi":"10.1109/TSIPN.2024.3487421","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3487421","url":null,"abstract":"Distributed learning and adaptation have received significant interest and found wide-ranging applications in machine learning and signal processing. While various approaches, such as shared-memory optimization, multi-task learning, and consensus-based learning (e.g., federated learning and learning over graphs), focus on optimizing either local costs or a global cost, there remains a need for further exploration of their interconnections. This paper specifically focuses on a scenario where agents collaborate towards a common task (i.e., optimizing a global cost equal to aggregated local costs) while effectively having distinct individual tasks (i.e., optimizing individual local parameters in a local cost). Each agent's actions can potentially impact other agents' performance through interactions. Notably, each agent has access to only its local zeroth-order oracle (i.e., cost function value) and shares scalar values, rather than gradient vectors, with other agents, leading to communication bandwidth efficiency and agent privacy. Agents employ zeroth-order optimization to update their parameters, and the asynchronous message-passing between them is subject to bounded but possibly random communication delays. This paper presents theoretical convergence analyses and establishes a convergence rate for nonconvex problems. Furthermore, it addresses the relevant use-case of deep learning-based resource allocation in communication networks and conducts numerical experiments in which agents, acting as transmitters, collaboratively train their individual policies to maximize a global reward, e.g., a sum of data rates.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"916-931"},"PeriodicalIF":3.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Sequential State Estimation Over Binary Sensor Networks With Inaccurate Process Noise Covariance: A Variational Bayesian Framework 具有不准确过程噪声协方差的二值传感器网络上的分布式顺序状态估计：一个变分贝叶斯框架

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-13 DOI: 10.1109/TSIPN.2024.3497773

Jiayi Zhang;Guoliang Wei;Derui Ding;Yamei Ju

{"title":"Distributed Sequential State Estimation Over Binary Sensor Networks With Inaccurate Process Noise Covariance: A Variational Bayesian Framework","authors":"Jiayi Zhang;Guoliang Wei;Derui Ding;Yamei Ju","doi":"10.1109/TSIPN.2024.3497773","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3497773","url":null,"abstract":"In this paper, the distributed sequential state estimation problem is addressed for a class of discrete time-varying systems with inaccurate process noise covariance over binary sensor networks. First, with the purpose of reducing communication costs, a special class of sensors called binary sensors, which output only one bit of data, is adopted. The Gaussian tail function is then used to describe the likelihood of the binary measurements. Subsequently, the process noise covariance matrix is modeled as a inverse Wishart distribution. By employing a variational Bayesian approach combined with diffusion filtering strategies, the parameters (i.e., mean and variance) of the prior and posterior probability density functions are formalized for the sequential estimator and the sequential predictor. Then, the fixed-point iteration is utilized to receive the approximate optimal distributions of both system states and estimated covariance matrices. Finally, a simulation example of target tracking demonstrates that our algorithm performs effectively using binary measurement outputs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1-10"},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks 网络上的可变步长扩散偏差补偿 APV 算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-11 DOI: 10.1109/TSIPN.2024.3496255

Fuyi Huang;Shuting Yang;Sheng Zhang;Haiqiang Chen;Pengwei Wen

引用次数: 0

Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems 基于强化学习的受扰多代理系统事件触发约束遏制控制

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-08 DOI: 10.1109/TSIPN.2024.3487422

Daocheng Tang;Ning Pang;Xin Wang

{"title":"Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems","authors":"Daocheng Tang;Ning Pang;Xin Wang","doi":"10.1109/TSIPN.2024.3487422","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3487422","url":null,"abstract":"This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"820-832"},"PeriodicalIF":3.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite-Time Performance Mask Function-Based Distributed Privacy-Preserving Consensus: Case Study on Optimal Dispatch of Energy System 基于有限时间性能掩码函数的分布式隐私保护共识：能源系统优化调度案例研究

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-06 DOI: 10.1109/TSIPN.2024.3485480

Minxue Kong;Feifei Shen;Zhi Li;Xin Peng;Weimin Zhong

{"title":"Finite-Time Performance Mask Function-Based Distributed Privacy-Preserving Consensus: Case Study on Optimal Dispatch of Energy System","authors":"Minxue Kong;Feifei Shen;Zhi Li;Xin Peng;Weimin Zhong","doi":"10.1109/TSIPN.2024.3485480","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485480","url":null,"abstract":"Privacy-preserving consensus can address the information being leaked during distributed computing, encouraging its application in various scenarios. This paper investigates the finite-time privacy-preserving distributed optimal dispatch for energy systems (ESs). Firstly, a dynamic output mask function is designed to ensure that each node's internal state cannot be identified while accomplishing a distributed task. Second, two finite-time privacy-preserving consensus algorithms are presented, including leader–follower and average consensus algorithms. Under the proposed dynamic mask function, the proposed algorithms are local, allowing each node to protect its privacy by adopting the proposed dynamic output mask. The superiority of the proposed algorithm lies in its ability to achieve precise convergence while ensuring privacy protection. Third, the accurate value of the target state can be obtained after finite steps when processing and transmitting information. In addition, several conditions are presented for ensuring the convergence of the algorithms, which is not limited by special topologies such as undirected graphs and balanced graphs. Finally, an application that achieves the distributed optimal dispatch for the CCHP-based (Combined Cooling, Heating, and Power) ESs, and two examples illustrate that the algorithms can be effective access to economic optimization and excellent privacy performance.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"776-787"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discrete-Time Controllability of Cartesian Product Networks 笛卡尔产品网络的离散时间可控性

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-06 DOI: 10.1109/TSIPN.2024.3487411

Bo Liu;Mengjie Hu;Junjie Huang;Qiang Zhang;Yin Chen;Housheng Su

引用次数: 0

Generalized Simplicial Attention Neural Networks 广义简约注意神经网络

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-25 DOI: 10.1109/TSIPN.2024.3485473

Claudio Battiloro;Lucia Testa;Lorenzo Giusti;Stefania Sardellitti;Paolo Di Lorenzo;Sergio Barbarossa

{"title":"Generalized Simplicial Attention Neural Networks","authors":"Claudio Battiloro;Lucia Testa;Lorenzo Giusti;Stefania Sardellitti;Paolo Di Lorenzo;Sergio Barbarossa","doi":"10.1109/TSIPN.2024.3485473","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485473","url":null,"abstract":"Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes. For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers. Hinging on topological signal processing principles, we devise a series of principled self-attention mechanisms able to process data associated with simplices of various order, such as nodes, edges, triangles, and beyond. These schemes learn how to combine data associated with neighbor simplices of consecutive order in a task-oriented fashion, leveraging on the simplicial Dirac operator and its Dirac decomposition. We also prove that GSAN satisfies two fundamental properties: permutation equivariance and simplicial-awareness. Finally, we illustrate how our approach compares favorably with other simplicial and graph models when applied to several (inductive and transductive) tasks, such as trajectory prediction, missing data imputation, graph classification, and simplex prediction.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"833-850"},"PeriodicalIF":3.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0