AI-Driven Enhancements for Handover in Visible Light Communication Systems

Abstract

A modified genetic algorithm (MGA) optimization procedure, alongside time series machine learning (ML) classifiers, is proposed to minimize handovers in a digital twin-based visible light communication (VLC) system. Frequent handovers have a direct impact on the overall performance of the VLC system due to the inherent connection downtime of a handover process. The handover scheme proposed in this article considers the receiver trajectory information to minimize handovers, maintaining the system performance below the forward error correction limit. Simulation results indicate that the proposed scheme outperforms a power-based handover scheme, achieving handover reductions of 42.47%. Therefore, the MGA combined to the ML models approach is an effective means of minimizing handovers, as well as improving overall VLC system performance.