Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms

Ahmad Pouradabi and Amir Rastegarnia and Azam Khalili and Ali Farzamnia (2022) Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms.

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Official URL: https://ieeexplore.ieee.org/document/9936845

Abstract

In diffusion estimation of distributed networks two characteristic parameters are crucial, the speed of convergence and steady-state error. Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. On the contrary, the diffusion normalized subband adaptive filter (DNSAF) algorithm has faster convergence than DNLMS, but final steady-state error is higher. In this paper, the overall performance is improved by combining these algorithms. Convex combination of DNLMS / DNSAF has a quick convergence rate and little steadystate error. The introduced algorithms execute tracking more effectively than traditional algorithms, in addition. We use a number of experimental findings to show how well the suggested method performs.

Item Type:	Proceedings
Keyword:	distributed networks, diffusion estimation, convex combination, learning curve, DNSAF, DNLMS, convergence
Subjects:	L Education > LB Theory and practice of education > LB5-3640 Theory and practice of education > LB2300-2430 Higher education T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Department:	FACULTY > Faculty of Engineering
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	05 Nov 2024 14:22
Last Modified:	05 Nov 2024 14:22
URI:	https://eprints.ums.edu.my/id/eprint/41739

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