Self-adaptive anti-misalignment model for transceivers in hybrid (RF/VLC)-V2V network

In this paper, a self-adaptive anti-misalignment model for transceivers in hybrid radio frequency (RF) communication and visible light communication (VLC) vehicle-to-vehicle (V2V) network is firstly proposed to solve the transceiver misalignment (TM) between vehicles in adjacent lanes caused by vehicle states' changes. By the information on roads and the state of vehicles, traffic scenarios are divided into three categories: the static traffic scenario, the low-speed traffic scenario and the high-speed traffic scenario. By the vehicle behavior characteristics, the relationship between TMs and vehicle states in different traffic scenarios is established. By the relationship, the self-adaptive anti-misalignment model for transceivers is constructed. By the model, the TM can be predicted and the communication mode can be selected. By simulation, the effectiveness of the model is demonstrated. The simulation results show that the model has comparative advantages of preventing the VLC links' interruption and reducing communication modes’ handover numbers in different traffic scenarios.