Resource Allocation for RIS-ISAC Internet of Vehicles based on LSTM-DDPG

With the widespread application of artificial intelligence (AI) in Internet of Vehicles (IoV), particularly in autonomous driving and intelligent traffic management. IoV is facing tremendous pressure for large amounts of data transmission and real-time perception. As a key technology of 6G, integrated sensing and communication (ISAC) is expected to alleviate this pressure by improving spectrum utilization and reducing transmission latency. In addition, when obstacles exist in IoV, communication performance is seriously affected, which is detrimental to driving safety. To address this issue, Reconfigurable Intelligent Surfaces (RIS) as a relay is a feasible solution. Therefore, we construct a RIS-assisted ISAC IoV scenario, and further consider adding dynamic obstacles. Our optimization problem aims to enhance overall performance by maximizing a weighted combination of communication rate and sensing accuracy through the optimization of beamforming and RIS phase shifts. The problem has temporal characteristics and is a Markov Decision Process (MDP). To this end, we use the Long Short-Term Memory-Deep Deterministic Policy Gradient (LSTM-DDPG) algorithm to solve the problem. The results from the simulation illustrate the efficacy of the proposed algorithm in handling dynamic and complex blockage scenarios. Additionally, introducing RIS in blockage scenarios significantly increases the communication rate by up to 58.33%.