Path Assignment in Mesh Networks at the Edge of Wireless Networks

2024-11-15

arXiv.org

AI Summary

This paper addresses the path selection problem from edge users to the core network in wireless mesh networks. We propose an interference-aware tree-search path optimization algorithm designed to maximize the end-to-end signal-to-noise-plus-interference ratio (SNIR). Unlike conventional approaches that neglect interference, our method is the first to embed a global wireless interference model directly into the tree-search framework—thereby preserving solution quality while substantially reducing computational complexity. Experimental evaluation across three mesh network scales demonstrates that the selected paths achieve a minimum SNIR 3–18 dB higher than those produced by interference-agnostic methods, and 16–20 dB and 0.5–7 dB higher than those of random and genetic algorithms, respectively; moreover, our algorithm incurs significantly lower runtime overhead. This work establishes a new paradigm for efficient, scalable path assignment in high-interference edge networking environments.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Social mining and social search on the Web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizing path assignment in wireless mesh networks
• Maximizing signal-to-noise-plus-interference ratio (SNIR)
• Reducing computational complexity in path selection

Innovation

• Tree-search-based algorithm for path assignment
• Maximizes signal-to-noise-plus-interference ratio (SNIR)
• Lower complexity than genetic algorithm (GA)