DRL-Based Multidimensional Resource Scheduling for Intelligent Connected Vehicles in UAV-Assisted VEC Systems

Uncrewed aerial vehicle (UAV)-assisted vehicular edge computing (VEC) has emerged as a novel paradigm for compute-intensive and latency-sensitive tasks for intelligent connected vehicles (ICVs) by introducing UAVs to the vehicular network. However, in the temporary hotspot scenario with traffic congestion, due to the high-speed mobility of vehicles, effective solutions that support vehicles’ higher quality of service (QoS) remain a significant challenge. Unlike previous works, we first investigated the UAV deployment problem of maximizing the transmission rate and proposed a dense boundary prioritized service (DBPS) algorithm to address it. We then investigated the multidimensional resource scheduling problem of minimizing the weighting of system energy consumption and latency. Considering the time computing and communication resource, we proposed a mixed noise hindsight experience replay-deep deterministic policy gradient (MNHER-DDPG) algorithm to address it, which improved the DDPG algorithm in exploring noise and experience replay. Finally, experiment results show that the DBPS algorithm enhances the transmission rate, and the MNHER-DDPG algorithm improves the system’s energy consumption and latency.