Partial-Diffusion Least Mean-Square Estimation Over Networks Under Noisy Information Exchange

TL;DR

This paper analyzes the impact of noisy information exchanges on the partial-diffusion LMS algorithm in adaptive networks, highlighting the trade-offs between communication efficiency and estimation accuracy.

Contribution

It extends the partial-diffusion LMS framework to account for noisy links, providing analysis of perturbation effects and trade-offs in realistic noisy environments.

Findings

Noisy links significantly affect estimation performance.

Trade-off between communication cost and accuracy becomes unbalanced.

Simulation confirms the impact of link noise on algorithm effectiveness.

Abstract

Partial diffusion scheme is an effective method for reducing computational load and power consumption in adaptive network implementation. The Information is exchanged among the nodes, usually over noisy links. In this paper, we consider a general version of partial-diffusion least-mean-square (PDLMS) algorithm in the presence of various sources of imperfect information exchanges. Like the established PDLMS, we consider two different schemes to select the entries, sequential and stochastic, for transmission at each iteration. Our objective is to analyze the aggregate effect of these perturbations on general PDLMS strategies. Simulation results demonstrate that considering noisy link assumption adds a new complexity to the related optimization problem and the trade-off between communication cost and estimation performance in comparison to ideal case becomes unbalanced.