Improved Multi-Agent Proximal Policy Optimization Algorithm for Resource Allocation with Radar-Perception in UAV-Assisted VEC

In congested road environments, the spectrum resources available for Roadside Units (RSUs) are often insufficient to meet the communication needs of a large number of users simultaneously. To address this, Unmanned Aerial Vehicles (UAVs) can be deployed to supplement spectrum resources temporarily. This article proposes a UAV-assisted Vehicle Edge Computing (VEC) system, integrating UAVs to enhance RSU capabilities in congested scenarios. Traditional spectrum sensing techniques, however, struggle to autonomously monitor vehicular movements and maintain stable spectrum performance. To overcome this, we introduce radar sensing devices into the RSUs to improve perception accuracy and consistency. The integration of radar sensors, while beneficial, creates additional competition for limited system resources. We, therefore, formulate the resource allocation problem considering computation delay, communication rate, and perception data, constrained by spectrum resources, offloading decisions, and time-slot allocations. The problem is modeled as a Markov Decision Process (MDP), and we propose an Improved Multi-Agent Proximal Policy Optimization (IMAPPO) algorithm to optimize resource allocation under these constraints. The experimental results show that compared to baseline algorithms such as A3C, our proposed algorithm reduces the average task processing delay by 15.53%, increases the radar estimation mutual information (MI) by 9.52%, and improves the task completion rate by 4.1%.