The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

中文翻译：

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无人机启用无线网络的多智能体协作学习

无人机 (UAV) 技术为可扩展的无线边缘网络提供了潜在的解决方案。本文使用两架具有加速运动和固定高度的无人机来实现无线边缘网络，其中一架无人机将下行信号转发给分布在一个区域内的用户终端（UT），而另一架则收集上行数据。考虑到 UT 的活动概率和两个 UAV 的服务队列，推导出上行和下行传输的条件平均可实现速率及其下限。此外，提出了一个旨在最大化整个系统的能源效率的问题，其中考虑了通信相关的能源和推进能源消耗。然后，我们开发了一种新的多智能体 Q 学习 (MA-QL) 算法来最大化能源效率，通过优化无人机的轨迹和发射功率。最后，通过仿真结果验证了我们的分析，并检验了不同参数对下行链路和上行链路可实现速率、无人机能耗和系统能效的影响。结果表明，所提出的算法比其他基准方案实现了更高的能效。