  Predictive millimeter-wave (mmWave) beamforming is a promising technique to
enable low-latency and high-rate ground-air communications for
cellular-connected unmanned aerial vehicles (UAVs). However, the high
vulnerability of mmWave to blockages poses practical challenges to the
implementation of such a technology. In this paper, we tackle the challenges by
proposing a channel knowledge map (CKM)-assisted predictive beamforming
approach based on the echoed joint communication and sensing signal, whereby
the line-of-sight (LoS) link identification is performed via hypothesis testing
using prior information provided by CKM. Depending on the identification
result, extended Kalman filtering (EKF) is adopted to reliably track the target
UAV. Furthermore, if the non-line-of-sight (NLoS) state is identified, the
target UAV will be immediately connected to a candidate base station (BS),
namely a handover will be triggered to alleviate the communication outage. The
simulation results show that the proposed method can significantly enhance the
UAV tracking and mmWave communication performance compared to the benchmarking
schemes without using CKM or LoS identification.
