Sum Rate Optimisation for IRS-Assisted VLC Systems with Time Delay Considerations Using Deep Q-Learning
TL;DR
A deep Q-Learning (DQL) framework for resource optimisation in intelligent reflecting surfaces (IRS)-assisted VLC systems and results support the viability of learning-based, delay-aware optimisation for next-generation intelligent indoor VLC networks.
Abstract
Visible light communication (VLC) is a promising solution for high-speed indoor wireless connectivity, offering advantages such as license-free spectrum and enhanced physical-layer security. However, VLC performance is highly dependent on line-of-sight (LoS) conditions and is vulnerable to signal degradation caused by device orientation and dynamic obstructions. To address these challenges, this paper proposes a deep Q-Learning (DQL) framework for resource optimisation in intelligent reflecting surfaces (IRS)-assisted VLC systems. A realistic system model is developed that incorporates both LoS and non-line-of-sight (NLoS) com-ponents, while explicitly modeling frequency-domain effects due to IRS-induced time delay. The optimisation problem jointly considers IRS element allocation and user-LED association, aiming to maximise the system sum rate under practical constraints such as quality of service and IRS-induced time delay. A DQL algorithm is designed to learn efficient allocation strategies in high-dimensional dynamic environments. Simulation results show that the proposed DQL approach closely matches the achievable rate predicted by analytical models. Furthermore, the results highlight the critical impact of accounting for time delay and user orienta-tion when designing IRS-assisted VLC systems. The findings support the viability of learning-based, delay-aware optimisation for next-generation intelligent indoor VLC networks.