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.
Cite
@article{arxiv.2602.03526,
title = {Q-Learning for 3D Coverage in VCSEL-based Optical Wireless Systems},
author = {Hossein Safi and Rizwana Ahmad and Iman Tavakkolnia and Harald Haas},
journal= {arXiv preprint arXiv:2602.03526},
year = {2026}
}
Comments
Accepted for oral presentation at the IEEE ICC 2026 symposium