Resource Allocation for Vehicle Platooning in 5G NR-V2X via Deep Reinforcement Learning (2101.10424v2)

Abstract: Vehicle platooning, one of the advanced services supported by 5G NR-V2X, improves traffic efficiency in the connected intelligent transportation systems (C-ITSs). However, the packet delivery ratio of platoon communication, especially in the out-of-coverage area, is significantly impacted by the random selection algorithms employed in the current resource allocation scheme. In this paper, we first analyze the collision probability via the random selection algorithm adopted in the current standard. Subsequently, we then investigate the deep reinforcement learning (DRL) algorithm that decreases the collision probability by letting the agent (vehicle) learn from the communication environment. Monte Carlo simulation is utilized to verify the results obtained in the analytical model and to compare the results between the two discussed algorithms. Numerical results show that the proposed DRL algorithm outperforms the random selection algorithm in terms of different vehicle density, which at least lowering the collision probability by 73% and 45% in low and high vehicle density respectively.