Q-Learning for 3D Coverage in VCSEL-based Optical Wireless Systems

TL;DR

This paper presents a reinforcement learning approach to dynamically adjust beam divergence in VCSEL-based optical wireless systems, significantly improving coverage and robustness without complex modeling.

Contribution

Introduces a RL-based framework for real-time divergence adaptation in VCSEL OWC networks, eliminating the need for analytical models or exhaustive searches.

Findings

Achieves up to 92% coverage at low receiver heights

Maintains robust performance under challenging conditions

Enables scalable, real-time beam control

Abstract

Beam divergence control is a key factor in maintaining reliable coverage in indoor optical wireless communication (OWC) systems as receiver height varies.Conventional systems employ fixed divergence angles, which result in significant coverage degradation due to the non-convex tradeoff between optical power concentration and spatial spread. In this paper, we introduce a reinforcement learning (RL)-based framework for dynamic divergence adaptation in vertical-cavity surface-emitting laser (VCSEL)-based OWC networks. By continuously interacting with the environment, the RL agent autonomously learns a near-optimal mapping between receiver height and beam divergence, thereby eliminating the need for analytical modeling or computationally intensive exhaustive search. Simulation results demonstrate that the proposed approach achieves up to 92% coverage at low receiver heights and maintains robust performance under challenging conditions, enabling scalable, real-time, and energy-efficient beam control for dense VCSEL array deployments in next-generation OWC systems.