Path Assignment in Mesh Networks at the Edge of Wireless Networks

TL;DR

This paper introduces a novel tree-search-based algorithm for path assignment in wireless mesh networks at the network edge, optimizing SNIR and outperforming existing algorithms in signal quality and computational efficiency.

Contribution

The paper presents a new tree-search algorithm that improves path selection by maximizing SNIR, outperforming traditional and genetic algorithms in signal quality and complexity.

Findings

SNIR values are 3-18 dB higher than interference-ignoring algorithms.

SNIR values are 16-20 dB higher than random path selection.

Our algorithm has lower computational complexity than genetic algorithms.

Abstract

We consider a mesh network at the edge of a wireless network that connects users to the core network via multiple base stations. For this scenario, we present a novel tree-search-based algorithm that strives to identify effective communication path to the core network for each user by maximizing the signal-to-noise-plus-interference ratio (SNIR) along the chosen path. We show that, for three mesh networks of varying sizes, our algorithm selects paths with minimum SNIR values that are 3 dB to 18 dB higher than those obtained through an algorithm that disregards interference within the network, 16 dB to 20 dB higher than those chosen randomly by a random path selection algorithm, and 0.5 dB to 7 dB higher compared to a recently introduced genetic algorithm (GA). Furthermore, we demonstrate that our algorithm has lower computational complexity compared to the GA in networks where its performance is within 2 dB of ours.