In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV). To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller. Considering the overlong solving time of the formulated problem and the sensitive delay requirements of vehicular applications, we transform the optimization problem using reinforcement learning and then design a deep deterministic policy gradient (DDPG)-based solution. Through training the DDPG-based resource management model offline, optimal vehicle association and resource allocation decisions can be obtained rapidly. Simulation results demonstrate that the DDPG-based resource management scheme can converge within 200 episodes and achieve higher delay/quality-of-service satisfaction ratios than the random scheme.

中文翻译：

---

基于 DDPG 的 MEC/UAV 辅助车载网络资源管理

在本文中，我们研究了多路访问边缘计算（MEC）和无人机（UAV）辅助的车载网络中的联合车辆关联和多维资源管理。为了有效地管理 MEC 安装基站和无人机的可用频谱、计算和缓存资源，在中央控制器制定并执行资源优化问题。考虑到公式化问题的求解时间过长和车载应用的敏感延迟要求，我们使用强化学习对优化问题进行了转换，然后设计了基于深度确定性策略梯度 (DDPG) 的解决方案。通过离线训练基于DDPG的资源管理模型，可以快速获得最优的车辆关联和资源分配决策。