This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (i.e., UAVs work as mobile base stations). The primary objective of the proposed algorithm is to establish dependable mobile access networks for cellular vehicle-to-everything (C-V2X) communication, thereby facilitating the realization of high-quality intelligent transportation systems (ITS). 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 comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted in this paper. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms.

翻译：本文提出了一种新颖的基于多智能体深度强化学习(MADRL)的多无人机(UAV)协作定位算法（即无人机作为移动基站）。该算法的首要目标是为蜂窝车联网(C-V2X)通信建立可靠的移动接入网络，从而助力实现高质量智能交通系统(ITS)。可靠的移动接入服务可通过以下两种方式实现：i) 节能型无人机运行；ii) 可靠的无线通信服务。在节能型无人机运行方面，所提MADRL算法的奖励函数包含无人机能量消耗模型特征，以实现高效运行。此外，在可靠无线通信服务方面，将单个用户的服务质量(QoS)需求作为奖励的一部分，并采用60GHz毫米波无线电进行移动接入。本文采用60GHz毫米波接入以利用以下优势：i) 超宽带实现多吉比特每秒高速通信；ii) 高定向通信实现空间复用，这对密集部署的用户场景尤为有利。最后，本文对所提基于MADRL的多无人机定位算法进行了全面且数据密集型的性能评估。评估结果表明，所提算法优于现有其他算法。