This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based multiple unmanned aerial vehicles (UAV) positioning algorithm for reliable mobile access services (i.e., UAVs work as mobile base stations), where the MADRL is designed by the concept of centralized training and distributed execution (CTDE) for multi-agent cooperation and coordination. The reliable mobile access services can be achieved in following two ways, i.e., (i) energy-efficient UAV operation and (ii) reliable wireless communication services. For energy-efficient UAV operation, the reward of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of rewards and 60GHz mmWave radio is used for mobile access. This paper considers the 60GHz mmWave access for utilizing the benefits of (i) ultra-wide-bandwidth for multi-Gbps high-speed communications and (ii) high-directional communications for spatial reuse that is obviously good for densely deployed users. Lastly, the performance of our proposed MADRL-based multi-UAV positioning algorithm is evaluated; and it can be confirmed that the proposed algorithm outperforms the other existing algorithms.
翻译:本文论述基于多剂深度强化学习(MADRL)的多无人驾驶飞行器(UAV)定位算法,用于可靠的移动接入服务(即无人驾驶飞行器作为移动基地站),MADRL是根据集中培训和分散执行(CTDE)的概念设计的,用于多剂合作与协调,可靠的移动接入服务可以通过以下两种方式实现,即(一) 节能无人驾驶飞行器操作和(二) 可靠的无线通信服务。对于节能无人驾驶飞行器操作,我们拟议的MADRL算法的奖励包括无人驾驶飞行器能源消费模型的特征,以便实现高效运行。此外,对于可靠的无线通信服务,个人用户的服务质量(QOS)被视为奖励的一部分,而60GHzmmWave无线电用于移动接入。本文认为,60GHzmwave访问可通过以下两种方式获得好处:(一) 超宽频带,用于多Gbps高速通信,以及(二) 用于空间再利用的高级通信的高级直接通信,以便实现高效的运行。此外,关于可靠的无线通信服务质量(MADR)的要求显然是目前部署的甚级算算算算。