A novel approach of dynamic base station switching strategy based on Markov decision process for interference alignment in VANETs

In Vehicular Ad-hoc Networks (VANETs), high frequent interaction of safety-related information is required among vehicles and imposes urgent demand on information update. In order to reduce communication delay and improve the capacity of concurrent communication, in this paper we propose that the multi-antenna vehicle could takeover the channel management as dynamic base station to apply Multiple-Input Multiple-Output communication and interference management approach in Vehicle to Vehicle (V2V) communications. Firstly, we construct an Markov decision process (MDP) model for multi-antenna vehicle to estimate whether it is appropriate to be dynamic base station. In addiction, Monte Carlo Tree Search algorithm is introduced to derive MDP policy. Thirdly, the V2V Interference Alignment (V2V-IA) model is constructed for dynamic base station to obtain IA scheme to manage V2V communications and IA in VANETs. To achieve the goal of improving frequency of information update, we propose an optimized problem to minimize total number of time slots, which is required for completing global safety-related information delivery. Simulation results show that the frequency of information update can be improved effectively by the proposed approach and the average improvement could go up to 40%.