Sensing Communication Bandwidth Allocation in Vehicular Links Based on Reinforcement Learning

Influenced by the randomnesses in the realistic vehicle-to-infrastructure (V2I) scenarios, traditional deterministic optimization algorithms cannot be adopted directly to promote sensing-communication performance. In this letter, we consider a vehicle served by several base stations (BSs), model the integrated performance based on information theory, and formulate an optimization problem to enlarge the total information amount. A reinforcement-learning-based algorithm of jointly optimizing the bandwidth allocation and BS selection is proposed, where a double deep Q-network agent is adopted. Simulation results verify the system performance, which shows performing better than baselines.