Evaluation of Channel Assignment Performance Prediction Techniques in Random Wireless Mesh Networks

TL;DR

This paper evaluates the effectiveness of interference-based channel assignment performance prediction techniques in large, randomly deployed wireless mesh networks using extensive ns-3 simulations.

Contribution

It extends previous interference prediction methods to large, random WMNs and validates their reliability through comprehensive simulation studies.

Findings

Prediction techniques are effective in random WMNs.

Proposed methods reliably estimate CA performance.

Simulation results confirm applicability to large networks.

Abstract

Performance of wireless mesh networks (WMNs) in terms of network capacity, end-to-end latency, and network resilience depends upon the prevalent levels of interference. Thus, interference alleviation is a fundamental design concern in multi-radio multi-channel (MRMC) WMNs, and is achieved through a judicious channel assignment (CA) to the radios in a WMN. In our earlier works we have tried to address the problem of estimating the intensity of interference in a wireless network and predicting the performance of CA schemes based on the measure of the interference estimate. We have proposed reliable CA performance prediction approaches which have proven effective in grid WMNs. In this work, we further assess the reliability of these CA performance prediction techniques in a large MRMC WMN which comprises of randomly placed mesh routers. We perform exhaustive simulations on an ns-3 802.11n environment. We obtain conclusive results to demonstrate the efficacy of proposed schemes in random WMNs as well.