Distributed Least Mean Square Estimation With Communication Noises Over Random Graphs

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-19 DOI:10.1109/TSIPN.2025.3536103

Xiaozheng Fu;Siyu Xie;Tao Li

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引用次数: 0

Abstract

For the online distributed estimation problem of time-varying parameters, we study a linear regression model with measurement noises over time-varying random graphs. We propose a distributed normalized least mean square (LMS) algorithm, where each node updates its own estimate by the least mean square term, and sums the differences between its own estimate and the estimates of its neighbors with additive and multiplicative communication noises by the consensus term. By the algebraic graph theory and the stochastic analysis techniques, we obtain sufficient conditions for the boundedness of the tracking error. For a sequence of general random graphs, if the random graphs and the regression matrices satisfy the stochastic spatio-temporal persistence of excitation condition, then the mean-square tracking error is bounded by choosing appropriate constant gains. Furthermore, for conditional balanced graphs and Markovian switching graphs, we give sufficient conditions such that the persistence of excitation condition holds. Finally, we illustrate the effectiveness of the theoretical results through a numerical example.

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随机图上有通信噪声的分布式最小均方估计

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.