Deep Reinforcement Learning-Based Adaptive Handover Mechanism for VLC in a Hybrid 6G Network Architecture

Abstract

Visible light communication (VLC) is considered an important complementary technology for extremely high sixth-generation (6G) data transmission and has become part of a hybrid 6G indoor network architecture with an ultradense deployment of VLC access points (APs) that presents severe challenges to user mobility. An adaptive handover mechanism, which includes a seamless handover protocol and a selection algorithm optimized with a deep reinforcement learning (DRL) method, is proposed to overcome these challenges. Experimental simulation results reveal that the average downlink data rate with the proposed algorithm is up to 48% better than those with traditional RL algorithms and that this algorithm also outperforms the deep Q-network (DQN), Sarsa and Q-learning algorithms by 8%, 13% and 13%, respectively.