Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks

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

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

Abstract

This paper investigates the distributed estimation problem over networks with highly correlated and noisy inputs. As a first step, this paper proposes an algorithm based on diffusion affine projection Versoria (APV) that can process highly correlated input signals over networks. Following that, the optimal step-size is derived by minimizing the mean-square deviation at each node, so that the tradeoff between convergence rate and steady-state error can be addressed. To reduce estimation bias caused by input noise, two diffusion bias-compensated APV (DBCAPV) algorithms are then developed by solving the asymptotic unbiasedness or local constrained optimization problems. Using the optimal step-size processed through the moving average and reset mechanisms, two variable step-size DBCAPV algorithms are obtained. The simulation results demonstrate that our methods are effective.

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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.