Coordinated Beamforming for UAV-Aided Millimeter-Wave Communications Using GPML-Based Channel Estimation

In the 5th generation (5G) networks, coordinated multiple point (CoMP) is one of key technologies to improve the quality of service (QoS) of edge users. To meet the requirement of growing data rates, millimeter-wave (mmWave) can be employed in the CoMP system. However, the QoS of users may be degraded if line-of-sight (LoS) mmWave channels are not guaranteed. In this article, an unmanned aerial vehicle (UAV)-aided communication scheme is proposed to enhance the QoS of edge users, where the UAV helps a primary base station (BS) and a coordinated BS simultaneously. In the proposed scheme, since the UAV only feeds back the channel state information (CSI) to the primary BS, the CSI obtained at the coordinated BS through a backbone network becomes outdated. In order to overcome the performance loss caused by the CSI feedback delay, a machine learning based channel estimation scheme is studied for the coordinated BS to perform hybrid beamforming. Furthermore, to eliminate the inter-BS interference, a maximize signal to interference-plus-noise ratio (Max-SINR) based beamforming compensation scheme is proposed for the primary BS and UAV. The simulation results show that both the bit error rate (BER) and sum rate performance can be improved by employing the proposed schemes.