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Deep Reinforcement Learning for Multi-User Access Control in UAV Networks

机译：无人机网络中的多用户访问控制的深度强化学习

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摘要

Unmanned Aerial Vehicles (UAVs) have recently been proposed as flying base stations, called UAV-BSs, to provide reliable connections and extend the coverage of the existing wireless networks. The mobility of UAV-BSs leads to a dynamic network environment, in which the global network information is hard to be obtained. Since frequent information exchanges cause huge signaling overheads, it is difficult to deploy centralized algorithms in UAV networks. Hence, we propose a distributed deep reinforcement learning (DRL) framework for multi-user access control in UAV networks. In particular, each user makes its own access decisions independently based on the local network information, and maximizes the long-term throughput while avoiding frequent handovers. Simulation results have validated the effectiveness of the proposed algorithm and shown the superiority of the proposed DRL framework over the state of arts.
机译：最近有人提议将无人飞行器（UAV）作为飞行基站，称为UAV-BS，以提供可靠的连接并扩展现有无线网络的覆盖范围。 UAV-BS的移动性导致动态网络环境，其中难以获得全球网络信息。由于频繁的信息交换会导致巨大的信令开销，因此难以在UAV网络中部署集中式算法。因此，我们提出了一种分布式深度强化学习（DRL）框架，用于无人机网络中的多用户访问控制。特别是，每个用户都基于本地网络信息独立地做出自己的访问决策，并在避免频繁切换的同时最大化长期吞吐量。仿真结果验证了该算法的有效性，并显示了所提出的DRL框架相对于现有技术的优越性。

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