  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.
