Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes' movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT.

翻译：尽管无线连接正向普适化发展，但在自然灾害和临时性人群聚集等场景中，通信基础设施可能受损，无线覆盖不足甚至完全缺失。由无人飞行器组成的飞行网络作为一种灵活且经济的解决方案，可在上述场景中按需提供无线连接。无人飞行器几乎可在任何地点运行，且不断增长的载荷能力使其成为搭载无线通信硬件的理想平台。当前飞行网络领域的研究主要聚焦于飞行节点的最优部署，而无线链路参数仍采用默认配置。另一方面，现有链路自适应算法主要针对固定或低移动性场景设计。本文提出一种面向飞行网络的速率自适应方法——轨迹感知速率自适应算法（TARA），该方法通过利用飞行节点移动轨迹的先验知识预测未来信道状态，并据此进行速率自适应。基于100条不同轨迹的仿真结果表明，与Minstrel-HT等传统速率自适应算法相比，该方案吞吐量最高可提升53%，平均提升幅度达14%。