Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications

Massive multiple-input multiple-output (MIMO) is a promising technology that can mitigate interference effectively in cellular-connected unmanned aerial vehicle (UAV) communications. In this letter, we propose a fusion of wireless and sensor data to enhance beam alignment for cellular-connected UAV massive MIMO communications. We develop a predictive beamforming framework, including the frame structure and predictive beamformer. Moreover, we employ an extended Kalman filter (EKF) to integrate channel and sensor data. Simulation results demonstrate that the proposed scheme can improve position/orientation estimation accuracy significantly, leading to higher spectral efficiency.