Guaranteeing Line-of-Sight Wireless Connectivity in Stochastic Environments with Random Obstacles

TL;DR

This paper proposes a graph-based method to optimize access point placement for guaranteed line-of-sight wireless connectivity in environments with randomly placed obstacles, reducing AP count while maintaining coverage.

Contribution

It introduces a novel stochastic environment model and clique-based algorithms to determine optimal AP placement for reliable LoS connectivity amidst obstacle variability.

Findings

25% reduction in required access points

Achieved 5% coverage gap with optimized placement

Demonstrated effectiveness in simulated stochastic environments

Abstract

Advancements in high-frequency communication technologies using millimeter waves (mmWave), Tera- Hertz (THz), and optical wireless frequency bands are key for extending wireless connectivity beyond 5G. These technologies offer a broader spectrum than the one available on low- and mid-bands, enabling ultra-high-speed data rates, higher device density, enhanced security, and improved positioning accuracy. However, their performance relies heavily on clear Line-of-Sight (LoS) conditions, as Non-LoS components are significantly weaker, making blockages a major challenge to ensure suitable received signal power. This paper addresses this limitation by identifying the minimum number and optimal placement of access points (APs) needed to ensure LoS connectivity in stochastic/dynamic environments with random obstacle locations. To achieve this, the stochastic environment is carefully modeled as a graph, where the nodes represent sub-polygons of layout realizations, and the edges capture the visibility overlaps between them. By employing maximal clique clustering and maximum clique packing methods over this graph, the proposed approach determines the AP placement locations that guarantee either full LoS coverage or controlled LoS gaps, while seamlessly adapting to the stochastic variability in obstacle locations. Simulations results in a representative stochastic environment demonstrate a 25% reduction in the required number of APs, achieving a tolerable 5% coverage gap compared to AP deployment optimized for full LoS coverage.