Data rate selection algorithms for Wi-Fi devices are an important area of research because they directly impact performance. Most of the proposals are based on measuring the transmission success probability for a given data rate. In dense scenarios, however, this probing approach will fail because frame collisions are misinterpreted as erroneous data rate selection. We propose FTMRate which uses the fine timing measurement (FTM) feature, recently introduced in IEEE 802.11. FTM allows stations to measure their distance from the AP. We argue that knowledge of the distance from the receiver can be useful in determining which data rate to use. We apply statistical learning (a form of machine learning) to estimate the distance based on measurements, estimate channel quality from the distance, and select data rates based on channel quality. We evaluate three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. We present a performance evaluation of the three variants of FTMRate and show, in several dense and mobile (though line-of-sight only) scenarios, that it can outperform two benchmarks and provide close to optimal results in IEEE 802.11ax networks.

翻译：Wi-Fi设备的数据速率选择算法是影响网络性能的关键研究领域。现有方案多基于测量特定数据速率的传输成功概率，然而在密集场景中，这种探测方法会因帧碰撞被误判为速率选择错误而失效。本文提出FTMRate方法，利用IEEE 802.11最新引入的精细时间测量（FTM）特性实现速率优化。FTM使站点能够测量与接入点（AP）间的距离。我们论证了接收端距离信息对数据速率决策的有效性：通过统计学习（机器学习的一种形式）基于测量值估计距离，进而评估信道质量，最终依据信道质量选择最优数据速率。本文评估了三种不同的估计方法：指数平滑、卡尔曼滤波与粒子滤波。通过三种FTMRate变体的性能对比实验证明，在多个密集移动（仅限视距场景）场景中，该方案不仅优于两种基准算法，更能在IEEE 802.11ax网络中取得接近理论最优的性能表现。