IEEE Transactions on Signal and Information Processing over Networks最新文献_第7页

Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework 基于斯塔克尔伯格博弈框架的分布式估算的信道定价和传感器调度

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-01-10 DOI: 10.1109/TSIPN.2024.3352271

Rui Tang;Wen Yang;Zhihai Rong;Chao Yang;Yang Tang

{"title":"Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework","authors":"Rui Tang;Wen Yang;Zhihai Rong;Chao Yang;Yang Tang","doi":"10.1109/TSIPN.2024.3352271","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352271","url":null,"abstract":"Since communication quality between sensors can directly affect distributed estimation, we consider the communication channel pricing and sensor scheduling problem for distributed estimation over a wireless sensor network with limited resources. Each sensor's choice of channels depends on its estimation performance and the channel communication cost which sets by a communication network server. Thus, there exists a tradeoff between the estimation accuracy and the channel communication cost. To solve this decision-making process, a Stackelberg game framework is builded, where the server firstly sets pricing strategy, then sensors schedule communication channels under limited resources. In this scenario, the existence of the optimal stationary decision-making process of sensors is provided after observing the server's stationary and deterministic pricing policy. Firstly, we analyze the impact of channel pricing on the convergence of the system. Then, the server's optimal pricing strategy is proposed after observing the sensors' channel scheduling policy under a Stackelberg game framework. The property of the equilibrium pair in the Stackelberg game framework is investigated and finally an optimal channel pricing and scheduling schemes based on the equilibrium pair is proposed. Finally, simulation results verify the optimality of the channel pricing and scheduling mechanisms.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"59-68"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139573462","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

IEEE Signal Processing Society Information 电气和电子工程师学会信号处理学会信息

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-01-10 DOI: 10.1109/TSIPN.2024.3349792

引用次数: 0

Distributed Model-Free Adaptive Predictive Control for MIMO Multi-Agent Systems With Deception Attack 具有欺骗攻击的多输入多输出多代理系统的分布式无模型自适应预测控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-01-01 DOI: 10.1109/TSIPN.2023.3346994

Zhenzhen Pan;Ronghu Chi;Zhongsheng Hou

引用次数: 0

A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity 针对数据异质性的集中/分散联合学习的本地化原点-二元方法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-25 DOI: 10.1109/TSIPN.2023.3343616

Iifan Tyou;Tomoya Murata;Takumi Fukami;Yuki Takezawa;Kenta Niwa

引用次数: 0

Learning Hypergraphs Tensor Representations From Data via t-HGSP 通过 t-HGSP 从数据中学习超图张量表示

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-20 DOI: 10.1109/TSIPN.2023.3345142

Karelia Pena-Pena;Lucas Taipe;Fuli Wang;Daniel L. Lau;Gonzalo R. Arce

{"title":"Learning Hypergraphs Tensor Representations From Data via t-HGSP","authors":"Karelia Pena-Pena;Lucas Taipe;Fuli Wang;Daniel L. Lau;Gonzalo R. Arce","doi":"10.1109/TSIPN.2023.3345142","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3345142","url":null,"abstract":"Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing. However, a meaningful hypergraph may only be available in specific cases. This paper addresses the challenge of learning the underlying hypergraph topology from the data itself. As in graph signal processing applications, we consider the case in which the data possesses certain regularity or smoothness on the hypergraph. To this end, our method builds on the novel tensor-based hypergraph signal processing framework (t-HGSP) that has recently emerged as a powerful tool for preserving the intrinsic high-order structure of data on hypergraphs. Given the hypergraph spectrum and frequency coefficient definitions within the t-HGSP framework, we propose a method to learn the hypergraph Laplacian from data by minimizing the total variation on the hypergraph (TVL-HGSP). Additionally, we introduce an alternative approach (PDL-HGSP) that improves the connectivity of the learned hypergraph without compromising sparsity and use primal-dual-based algorithms to reduce the computational complexity. Finally, we combine the proposed learning algorithms with novel tensor-based hypergraph convolutional neural networks to propose hypergraph learning-convolutional neural networks (t-HyperGLNN).","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"17-31"},"PeriodicalIF":3.2,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399599","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

Gradient-Based Spectral Embeddings of Random Dot Product Graphs 基于梯度的随机点积图谱嵌入

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-15 DOI: 10.1109/TSIPN.2023.3343607

Marcelo Fiori;Bernardo Marenco;Federico Larroca;Paola Bermolen;Gonzalo Mateos

{"title":"Gradient-Based Spectral Embeddings of Random Dot Product Graphs","authors":"Marcelo Fiori;Bernardo Marenco;Federico Larroca;Paola Bermolen;Gonzalo Mateos","doi":"10.1109/TSIPN.2023.3343607","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3343607","url":null,"abstract":"The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the \u0000<italic>embedding</i>\u0000 task of estimating these vectors from an observed graph is typically posed as a low-rank matrix factorization problem. The workhorse Adjacency Spectral Embedding (ASE) enjoys solid statistical properties, but it is formally solving a surrogate problem and can be computationally intensive. In this paper, we bring to bear recent advances in non-convex optimization and demonstrate their impact to RDPG inference. We advocate first-order gradient descent methods to better solve the embedding problem, and to organically accommodate broader network embedding applications of practical relevance. Notably, we argue that RDPG embeddings of directed graphs loose interpretability unless the factor matrices are constrained to have orthogonal columns. We thus develop a novel feasible optimization method in the resulting manifold. The effectiveness of the graph representation learning framework is demonstrated on reproducible experiments with both synthetic and real network data. Our open-source algorithm implementations are scalable, and unlike the ASE they are robust to missing edge data and can track slowly-varying latent positions from streaming graphs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"1-16"},"PeriodicalIF":3.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109661","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

Fixed-Time Optimal Fault-Tolerant Formation Control With Prescribed Performance for Fixed-Wing UAVs Under Dual Faults 双故障条件下具有规定性能的固定翼无人机固定时间最优容错编队控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-13 DOI: 10.1109/TSIPN.2023.3341406

Bo Meng;Ke Zhang;Bin Jiang

{"title":"Fixed-Time Optimal Fault-Tolerant Formation Control With Prescribed Performance for Fixed-Wing UAVs Under Dual Faults","authors":"Bo Meng;Ke Zhang;Bin Jiang","doi":"10.1109/TSIPN.2023.3341406","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3341406","url":null,"abstract":"This article aims to propose a novel fixed-time distributed optimized formation control scheme for fixed-wing unmanned aerial vehicles with uncertainties, communication link and actuator faults, and performance constraint. Firstly, the prescribed performance function is introduced to improve the steady-state and transient performances of fixed-wing UAVs system. Communication link faults are tolerated by utilizing the distributed leader state observer. Subsequently, with the objective of establishing optimal controllers for velocity and altitude subsystems, the reinforcement learning control method is employed. Simultaneously, an intermediate controller is constructed to tackle the difficulties in applying reinforcement learning to the fault-tolerant control scheme. In addition, new adaptive laws of fault factor parameters are proposed, which can make the fault-tolerant scheme align better with the concept of fixed-time convergence. Finally, fixed-time prescribed performance controllers for velocity and altitude subsystems are developed. The designed control algorithm can ensure that the velocity and altitude tracking errors converge to the prescribed region, and the simulation results further demonstrate that the proposed control strategy is effective.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"875-887"},"PeriodicalIF":3.2,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050589","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

An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks 无线传感器网络分布式检测的渐近等效 GLRT 检验

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-12 DOI: 10.1109/TSIPN.2023.3341407

Juan Augusto Maya;Leonardo Rey Vega;Andrea M. Tonello

{"title":"An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks","authors":"Juan Augusto Maya;Leonardo Rey Vega;Andrea M. Tonello","doi":"10.1109/TSIPN.2023.3341407","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3341407","url":null,"abstract":"In this article, we tackle the problem of distributed detection of a radio source emitting a signal. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively a statistic to decide if the source is present or absent. We model the radio source as a stochastic signal and work with spatially statistically dependent measurements. We consider the Generalized Likelihood Ratio Test (GLRT) approach to deal with an unknown multidimensional parameter from the model. We analytically characterize the asymptotic distribution of the statistic when the amount of sensor measurements tends to infinity. Moreover, as the GLRT is not amenable for distributed settings because of the spatial statistical dependence of the measurements, we study a GLRT-like test where the statistical dependence is completely discarded for building this test. Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistical dependence of the measurements has no impact on the detection performance in the asymptotic scenario. Furthermore, the GLRT-like algorithm has a low computational complexity and demands low communication resources, as compared to the GLRT.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"888-900"},"PeriodicalIF":3.2,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10354397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Event-Triggered Fusion Filtering for Multi-Sensor Rectangular Descriptor Systems With Random State Delay 具有随机状态延迟的多传感器矩形描述符系统的动态事件触发融合滤波

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-12 DOI: 10.1109/TSIPN.2023.3341410

Jun Hu;Ruonan Luo;Hongli Dong;Cai Chen;Hongjian Liu

{"title":"Dynamic Event-Triggered Fusion Filtering for Multi-Sensor Rectangular Descriptor Systems With Random State Delay","authors":"Jun Hu;Ruonan Luo;Hongli Dong;Cai Chen;Hongjian Liu","doi":"10.1109/TSIPN.2023.3341410","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3341410","url":null,"abstract":"This paper investigates the dynamic event-triggered fusion filtering problem for a class of uncertain multi-sensor rectangular descriptor systems with random state delay. The random state delay is depicted by a Bernoulli distributed random variable. In order to save the communication energy, a dynamic event-triggered mechanism (DETM) is employed to decide whether the measurements are transmitted to the local estimators. Firstly, by introducing the full-order transformation method, the rectangular descriptor systems are converted into the non-descriptor systems with full orders. Secondly, the local filter gains are designed to minimize the upper bounds of filtering error covariances (FECs), where the upper bounds of FECs and the filter gains depend on a group of free positive scalar parameters. To minimize the upper bounds of FECs, the scalar parameters are sought optimally by a numerical method, where the scalars obtained after optimization are called optimal parameters. Subsequently, the fusion filter of the original descriptor system is given by the inverse covariance intersection (ICI) fusion technique. Finally, the effectiveness and advantages of the proposed fusion filtering algorithm are illustrated by providing the experiments with circuit system application.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"836-849"},"PeriodicalIF":3.2,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034118","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

Stability of Aggregation Graph Neural Networks 聚合图神经网络的稳定性

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-12 DOI: 10.1109/TSIPN.2023.3341408

Alejandro Parada-Mayorga;Zhiyang Wang;Fernando Gama;Alejandro Ribeiro

{"title":"Stability of Aggregation Graph Neural Networks","authors":"Alejandro Parada-Mayorga;Zhiyang Wang;Fernando Gama;Alejandro Ribeiro","doi":"10.1109/TSIPN.2023.3341408","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3341408","url":null,"abstract":"In this paper we study the stability properties of aggregation graph neural networks (Agg-GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid architecture where information is defined on the nodes of a graph, but it is processed block-wise by Euclidean CNNs on the nodes after several diffusions on the graph shift operator. We derive stability bounds for the mapping operator associated to a generic Agg-GNN, and we specify conditions under which such operators can be stable to deformations. We prove that the stability bounds are defined by the properties of the filters in the first layer of the CNN that acts on each node. Additionally, we show that there is a close relationship between the number of aggregations, the filter's selectivity, and the size of the stability constants. We also conclude that in Agg-GNNs the selectivity of the mapping operators can be limited by the stability restrictions imposed on the first layer of the CNN stage, but this is compensated by the pointwise nonlinearities and filters in subsequent layers which are not subject to any restriction. This shows a substantial difference with respect to the stability properties of selection GNNs, where the selectivity of the filters in all layers is constrained by their stability. We provide numerical evidence corroborating the results derived, testing the behavior of Agg-GNNs in real life application scenarios considering perturbations of different magnitude.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"850-864"},"PeriodicalIF":3.2,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034299","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