Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation

TL;DR

This paper introduces a statistical interference estimation metric to predict the performance of channel assignment schemes in Wireless Mesh Networks, validated through extensive ns-3 simulations to aid in optimal CA selection.

Contribution

It proposes a novel interference characterization and a predictive metric for channel assignment performance in WMNs, addressing the lack of such tools in existing research.

Findings

The proposed metric accurately predicts CA performance.

Validation shows strong correlation with simulation results.

The approach enhances channel selection efficiency.

Abstract

Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on wired infrastructure especially with the advent of the multi-radio multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer viz., augmented network capacity, uninterrupted connectivity and reduced latency, are depreciated by the detrimental effect of prevalent interference. Interference mitigation is thus a prime objective in WMN deployments. It is often accomplished through prudent channel allocation (CA) schemes which minimize the adverse impact of interference and enhance the network performance. However, a multitude of CA schemes have been proposed in research literature and absence of a CA performance prediction metric, which could aid in the selection of an efficient CA scheme for a given WMN, is often felt. In this work, we offer a fresh characterization of the interference endemic in wireless networks. We then propose a reliable CA performance prediction metric, which employs a statistical interference estimation approach. We carry out a rigorous quantitative assessment of the proposed metric by validating its CA performance predictions with experimental results, recorded from extensive simulations run on an ns-3 802.11g environment.