Resource Allocation and Trajectory Optimization in Multi-UAV Collaborative Vehicular Networks: An Extended Multiagent DRL Approach

Abstract

In vehicular networks enhanced by uncrewed aerial vehicles (UAVs), vehicle state information is efficiently collected, and traffic safety is assured. UAVs, serving as aerial base stations, enable vehicle network access and provide edge computing services in the absence of roadside units (RSUs). This study explores a multi-UAV-assisted vehicular network, where multiple UAVs collaboratively offer services to vehicles. The goal is to minimize task completion time by optimizing trajectory planning, spectrum resource allocation, and dynamic data offloading. An enhanced multiagent deep deterministic policy gradient (MADDPG) algorithm is introduced to address the optimization challenge in cooperative multi-UAV scenarios. Within this framework, each UAV, acting as an agent, devises strategies for movement, data offloading, and resource allocation based on the current states of vehicles and fellow UAVs. The simulation results reveal that the proposed algorithm improves task completion efficiency and ensures vehicle Quality of Service (QoS) over existing benchmarks.