Impact of network size on the performance of incremental LMS adaptive networks

TL;DR

This paper investigates how the size of incremental LMS adaptive networks affects their performance, revealing that larger networks perform better with ideal links but not necessarily with noisy links, supported by simulations.

Contribution

It provides a comparative analysis of network size effects on ILMS performance under ideal and noisy link conditions, highlighting the nuanced impact of network size.

Findings

Larger networks improve steady-state error with ideal links.

Noisy links negate the benefits of increased network size.

Simulations confirm theoretical insights.

Abstract

In this paper we study the impact of network size on the performance of incremental least mean square (ILMS) adaptive networks. Specifically, we consider two ILMS networks with different number of nodes and compare their performance in two different cases including (i) ideal links and (ii) noisy links. We show that when the links between nodes are ideal, increasing the network size improves the steady-state error. On the other hand, in the presence of noisy links, we see different behavior and the ILMS adaptive network with more nodes necessarily has not better steady-state performance. Simulation results are also provided to illustrate the discussions.