Research on Network Handover Based on User Movement Prediction in Visible Light Communication and Wi-Fi Heterogeneous Networks

This paper addresses the handover challenge in indoor visible light communication and Wi-Fi heterogeneous networks, proposing an adaptive handover strategy based on user trajectory prediction. Extracting meaningful and important location points from massive trajectory data for clustering, an improved hidden Markov model is used to predict the user’s next location by analyzing the patterns of the user’s historical mobile trajectory data. The Q-learning algorithm is then used to determine the optimal network handover based on the current network state, while a seamless handover protocol is introduced to ensure successful network transition and uninterrupted data transmission. Compared with the traditional STD-LTE handover scheme, the proposed algorithm can reduce vertical handover rates by up to 32% during fast walking. When indoor user connections increase, the algorithm can maintain high fairness and high throughput when indoor user connections increase, verifying that it is robust in different scenarios.