Diffusion Average-Estimate Bias-Compensated LMS Algorithms over Adaptive Networks Using Noisy Measurements

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number9161289
Pages (from-to)4643-4655
Journal / PublicationIEEE Transactions on Signal Processing
Volume68
Online published6 Aug 2020
Publication statusPublished - 2020

Abstract

In this paper, we consider the problem of distributed estimation over adaptive networks in the presence of noisy input, output and communication links. First, a diffusion average-estimate bias-compensated LMS (D-ABC-LMS) algorithm is devised for processing these noisy measurements. Different from the existing diffusion schemes, it consists of three steps: (i) bias-compensated weight update, (ii) average estimation and (iii) weight combination, where the second step utilizes a moving average technique to process imprecise exchange weights caused by communication link noise. Then, we analyze the stability and convergence of the D-ABC-LMS algorithm, and derive closed-form expressions to predict its steady-state mean-square deviation (MSD) and network MSD (NMSD). In addition, by introducing one more step via the adaptive mixture of the noisy exchange weight vectors and their denoised estimates, we propose a diffusion mixed average-estimate bias-compensated LMS (D-MABC-LMS) method for increasing the convergence rate of the D-ABC-LMS scheme. Finally, computer simulation results show the superiority of the proposed algorithms over previously reported techniques using noisy measurements over adaptive networks.

Research Area(s)

  • Adaptive network, average estimate, bias-compensation, noisy measurement