V2V Wireless Resource Allocation Algorithm Based on Multi-Objective Deep Reinforcement Learning

Due to the dynamic uncertainty, diversified service types and scarcity of wireless communication resources in the context of vehicle-to-everything, this paper explores the challenge of ensuring the requirement for multiple quality of service and the effective utilization of wireless resources in the scenario where V2N (vehicle-to-network) and V2V (vehicle-to-vehicle) links coexist and share spectrum in cellular V2X (vehicle-to-everything) networks. First, a multi-objective optimization problem is presented to model the decision-making process of channel selection and power control in cellular vehicle-to-everything. The problem considers the impact of dynamic changes in the network environment, aiming to make a balance between the performance of the V2V link (i.e., age of information, delay, and capacity) and the capacity of the V2N link. On this basis, V2V wireless resource allocation algorithm based on multi-objective deep reinforcement learning is also proposed for training neural networks to solve the problem. Through the trained neural network model, the Pareto frontier of the multi-objective optimization problem can be obtained. Simulation results demonstrate that the proposed algorithm can achieve the near-optimal performance for different communication links. Compared with several representative algorithms, the age of information for V2V link is reduced by 12.0% to 17.2%, the V2N link capacity is increased by 11.4% to 21.6%, the V2V link transmission success rate is increased by 4.6% to 13.9%, and the decision delay time is reduced by 10.6% to 20.3%.