The invention discloses a joint cache decision and trajectory optimization method under an unmanned aerial vehicle assisted internet of vehicles, and belongs to the technical field of wireless communication. The method is based on a differential evolution algorithm and a multi-agent near-end strategy optimization (MAPPO) algorithm, and the differential evolution algorithm and the multi-agent near-end strategy optimization (MAPPO) algorithm are jointly applied to an air-ground cooperative system. The core idea is that a server on the unmanned aerial vehicle obtains a dynamic caching strategy by means of a differential evolution algorithm through a historical request, then the unmanned aerial vehicle is mapped into an intelligent agent in MAPPO, and while caching service is performed for a vehicle request, interactive learning with the environment is performed, the unmanned aerial vehicle is guided to plan a path in real time, and minimization of system energy consumption is achieved. The whole algorithm has the characteristics of dynamic cache strategy adjustment and real-time learning optimization required by the system, can meet the request of time delay sensitive service under the condition of limited computing resources, reduces the use energy consumption of the mobile equipment under the multi-unmanned aerial vehicle assisted Internet of Vehicles, and realizes energy-saving and high-quality service.
本发明公开了一种无人机辅助车联网下的联合缓存决策和轨迹优化方法,属于无线通信技术领域。该方法基于差分进化算法和多智能体近端策略优化(MAPPO)算法,将两者联合运用到空地协同系统中。其核心思想是无人机上的服务器通过历史请求,借助差分进化算法得到动态缓存策略,然后将无人机映射为MAPPO中的智能体,在为车辆请求进行缓存服务的同时,与环境交互学习,指导自己实时规划路径,实现系统能耗的最小化。整个算法具有系统所需的动态调整缓存策略、实时学习优化的特征,能够在计算资源受限的情况下满足时延敏感型服务的请求,降低了多无人机辅助车联网下移动设备的使用能耗,实现节能、高质量服务。
Joint cache decision and trajectory optimization method under unmanned aerial vehicle assisted Internet of Vehicles
一种无人机辅助车联网下的联合缓存决策和轨迹优化方法
2023-10-03
Patent
Electronic Resource
Chinese
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