论文标题
无人机MU-MIMO网络的传感器辅助速率适应
Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks
论文作者
论文摘要
由多用户MIMO（MU-MIMO）技术推动，无人驾驶汽车（UAVS）最近作为一种有吸引力的无线通信范式出现，因为移动热点最近出现了。速率适应（RA）变得必不可少，以增强无人机通信的鲁棒性，以针对无人机迁移率诱导的通道方差。但是，现有的MU-MIMO RA算法主要设计用于相对稳定的通道相干时间的地面通信，在应对高度动态的空气到地面链路时，它会导致通道测量的Stalegenty和Sub-Priptimal速率选择。在本文中，我们提出了一种新的上行链路Mu-Mimo ra算法，该算法专用于低空无人机，该算法利用了用于飞行控制的固有固有的机载式传感器，而无需额外费用。我们提出了一种新型的通道预测算法，该算法利用传感器估计的飞行状态来协助每个客户的通道方向预测，并估算用户间干扰最佳速率。我们使用商业无人机提供设计的实现，并表明与最著名的RA算法相比，它的平均吞吐量为1.24 \ times和1.28 \ times，分别为2-和3- Antenna APS
Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24\times and 1.28\times compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively
