Steady-state Performance of Incremental LMS Strategies For Parameter Estimation Over Fading Wireless Channels

TL;DR

This paper analyzes how fading wireless channels affect the steady-state performance of incremental LMS algorithms, providing theoretical metrics and conditions for convergence verified by simulations.

Contribution

It derives steady-state performance metrics and convergence conditions for ILMS algorithms over fading channels, which was not previously characterized.

Findings

Theoretical expressions for MSD, EMSE, and MSE under fading channels.

Conditions for mean-square convergence of ILMS in fading environments.

Simulation results confirm the accuracy of the theoretical analysis.

Abstract

We study the effect of fading in the communication channels between nodes on the performance of the incremental least mean square (ILMS) algorithm. We derive steady-state performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE), and mean-square error (MSE). We obtain the sufficient conditions to ensure mean-square convergence, and verify our results through simulations. Simulation results show that our theoretical analysis closely matches the actual steady state performance.