论文标题
基于DDPG的MEC/UAV辅助车辆网络的资源管理
DDPG-based Resource Management for MEC/UAV-Assisted Vehicular Networks
论文作者
论文摘要
在本文中，我们研究了由多访问边缘计算（MEC）和无人机（UAV）辅助的车辆网络中的联合车辆协会和多维资源管理。为了有效地管理MEC安装的基站和无人机的可用频谱，计算和缓存资源，在中央控制器上制定了资源优化问题并进行了资源优化问题。考虑到制定问题的解决时间和车辆应用的敏感延迟要求，我们使用强化学习改变了优化问题，然后设计了基于深层的确定性策略梯度（DDPG）基于基于的解决方案。通过培训基于DDPG的资源管理模型离线，可以迅速获得最佳的车辆协会和资源分配决策。仿真结果表明，基于DDPG的资源管理方案可以在200集内收敛，并获得比随机方案更高的延迟/服务质量满意度比。
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.
