With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios.

翻译：随着连接需求的不断增长，无人机已成为下一代按需无线网络部署的重要组成部分。然而，当前的无人机定位方案通常忽略速率自适应算法的影响，或通过考虑理想且不可实现的速率自适应算法来简化其效果。本文提出基于速率自适应的强化学习飞行网关定位算法，这是一种应用深度Q学习算法的飞行网关定位方法，能够考虑底层速率自适应算法施加的动态数据速率。该算法旨在最大化服务一个或多个飞行接入点的飞行无线链路吞吐量，这些接入点进而为地面终端提供服务。性能评估结果表明，所提算法能够充分考虑底层速率自适应算法的影响，并在所有分析的静态和移动场景中实现最大吞吐量。