具有实时数据处理和灵活部署的优势,无人机辅助移动边缘计算系统在民用和军事领域都有广泛的应用。然而,由于能量有限,无人机通常很难长时间停留在空中并执行计算任务。在本文中,我们提出了一种结合移动边缘计算和无线电力传输技术的全双工空对空通信系统(A2ACS)模型,旨在有效降低无人机的计算延迟和能耗,同时确保无人机不会因能量不足而中断任务或离开工作区域。在这个系统中,UAV从外部空气边缘能量服务器(AEES)收集能量以给机载电池供电并将计算任务卸载到AEES以减少延迟。为了优化系统的性能并平衡四个目标,包括系统吞吐量,无人机低功率警报的数量,无人机接收的总能量和AEES的能量消耗,我们开发了一个多目标优化框架。考虑到AEES需要在动态环境中快速决策,提出了一种基于多智能体深度确定性策略梯度(MADDPG)的算法,优化AEES服务位置并控制能量转移的功率。在训练的同时,在给定优化权重条件的情况下,代理学习最优策略。此外,我们采用K-means算法来确定AEES和无人机之间的关联,以确保公平性。仿真实验结果表明,提出的多目标DDPG(MODDPG)算法比基线算法具有更好的性能,如遗传算法和其他深度强化学习算法。
With the advantages of real-time data processing and flexible deployment, unmanned aerial vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and military fields. However, due to limited energy, it is usually difficult for UAVs to stay in the air for long periods and to perform computational tasks. In this paper, we propose a full-duplex air-to-air communication system (A2ACS) model combining mobile edge computing and wireless power transfer technologies, aiming to effectively reduce the computational latency and energy consumption of UAVs, while ensuring that the UAVs do not interrupt the mission or leave the work area due to insufficient energy. In this system, UAVs collect energy from external air-edge energy servers (AEESs) to power onboard batteries and offload computational tasks to AEESs to reduce latency. To optimize the system\'s performance and balance the four objectives, including the system throughput, the number of low-power alarms of UAVs, the total energy received by UAVs and the energy consumption of AEESs, we develop a multi-objective optimization framework. Considering that AEESs require rapid decision-making in a dynamic environment, an algorithm based on multi-agent deep deterministic policy gradient (MADDPG) is proposed, to optimize the AEESs\' service location and to control the power of energy transfer. While training, the agents learn the optimal policy given the optimization weight conditions. Furthermore, we adopt the K-means algorithm to determine the association between AEESs and UAVs to ensure fairness. Simulated experiment results show that the proposed MODDPG (multi-objective DDPG) algorithm has better performance than the baseline algorithms, such as the genetic algorithm and other deep reinforcement learning algorithms.