A joint power and bandwidth allocation method based on deep reinforcement learning for V2V communications in 5G

Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation system. When maintaining the high reliability and low latency in the vehicle-to-vehicle (V2V) links as well as large capacity in the vehicle-to-infrastructure (V2I) links, it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V communication. This paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization method. In this radio resources allocation framework, a vehicle or V2V link that is designed as an agent. And through interacting with the environment, it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power level. Because the proposed method can output continuous actions and multi-dimensional actions, it greatly reduces the implementation complexity of large-scale communication scenarios. The simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications.