Near Optimal Channel Assignment for Interference Mitigation in Wireless Mesh Networks

TL;DR

This paper introduces NOCAG, a heuristic channel assignment algorithm for grid wireless mesh networks that nearly matches brute-force optimal solutions with significantly reduced computational complexity.

Contribution

The paper presents a novel heuristic CA algorithm for grid WMNs that achieves near-optimal interference mitigation with minimal computational overhead.

Findings

NOCAG performs almost as well as brute-force CA in interference mitigation.

NOCAG has a computational complexity of O(n), much lower than brute-force methods.

Simulation results confirm NOCAG's effectiveness in real-world scenarios.

Abstract

In multi-radio multi-channel (MRMC) WMNs, interference alleviation is affected through several network design techniques e.g., channel assignment (CA), link scheduling, routing etc., intelligent CA schemes being the most effective tool for interference mitigation. CA in WMNs is an NP-Hard problem, and makes optimality a desired yet elusive goal in real-time deployments which are characterized by fast transmission and switching times and minimal end-to-end latency. The trade-off between optimal performance and minimal response times is often achieved through CA schemes that employ heuristics to propose efficient solutions. WMN configuration and physical layout are also crucial factors which decide network performance, and it has been demonstrated in numerous research works that rectangular/square grid WMNs outperform random or unplanned WMN deployments in terms of network capacity, latency, and network resilience. In this work, we propose a smart heuristic approach to devise a near-optimal CA algorithm for grid WMNs (NOCAG). We demonstrate the efficacy of NOCAG by evaluating its performance against the minimal-interference CA generated through a rudimentary brute-force technique (BFCA), for the same WMN configuration. We assess its ability to mitigate interference both, theoretically (through interference estimation metrics) and experimentally (by running rigorous simulations in NS-3). We demonstrate that the performance of NOCAG is almost as good as the BFCA, at a minimal computational overhead of O(n) compared to the exponential of BFCA.