  Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth
of civil and military applications. Robust and high-throughput wireless
communication is the crux of these UAV applications. Yet, air-to-ground links
suffer from time-varying channels induced by the agile mobility and dynamic
environments. Rate adaptation algorithms are generally used to choose the
optimal data rate based on the current channel conditions. State-of-the-art
approaches leverage physical layer information for rate adaptation, and they
work well under certain conditions. However, the above protocols still have
limitation under constantly changing flight states and environments for
air-to-ground links. To solve this problem, we propose StateRate, a
state-optimized rate adaptation algorithm that fully exploits the
characteristics of UAV systems using a hybrid deep learning model. The key
observation is that the rate adaptation strategy needs to be adjusted according
to motion-dependent channel models, which can be reflected by flight states. In
this work, the rate adaptation protocol is enhanced with the help of the
on-board sensors in UAVs. To make full use of the sensor data, we introduce a
learning-based prediction module by leveraging the internal state to
dynamically store temporal features under variable flight states. We also
present an online learning algorithm by employing the pre-trained model that
adapts the rate adaptation algorithm to different environments. We implement
our algorithm on a commercial UAV platform and evaluate it in various
environments. The results demonstrate that our system outperforms the
best-known rate adaptation algorithm up to 53% in terms of throughput when the
velocity is 2-6~m/s.
