Collaborative Online Learning-Based Distributed Handover Scheme in Hybrid VLC/RF 5G Systems

This paper investigates handover in hybrid visible light communication (VLC)/radio frequency (RF) networks. In such a network, mobile users are prone to experience frequent handovers (FHOs). To this end, we propose a collaborative online learning-based handover scheme (COLH) in hybrid VLC/RF 5G systems. By selecting the next access point (AP) to which a user should handover, our goal is to make the user–AP connection as long as possible after the handover, defined as a reward that is learned online through a multi-armed bandit (MAB) framework. Unlike previous schemes based on independent and collective learning, first, our scheme dynamically clusters users with similar feedback on a given AP. Second, the users in the same cluster collaborate in estimating the expected reward for that AP, and the one with the maximum expected reward is selected as the next AP. This scheme can be implemented without extensive offline training and location information; thus, its practicality is greatly enhanced. The simulation results show that the proposal outperforms existing benchmarks on reducing handovers.