Unscented Kalman Filter Based Beam Tracking for UAV-enabled Millimeter Wave Massive MIMO Systems

Fueled by the incessantly growing need of high-speed wireless access in the fifth generation (5G) wireless networks, unmanned aerial vehicle (UAV)-enabled millimeter wave (mmW) massive multiple input multiple output (mMIMO) has become an attractive solution to provide high capacity wireless transmission. In this paper, we proposed an unscented Kalman filter (UKF) based beam tracking scheme for the UAV-enabled mmW mMIMO systems to manage the challenge of rapid channel variation due to UAV's movement. Considering the nonlinearity of beamforming and the performance advantage of UKF to a broader nonlinear estimation problem, we utilize UKF to estimate the channel angle vector to obtain the channel state information which is indispensable for beamforming. Numerical results showed that the proposed beam tracking scheme was capable of improving the accuracy of the estimated beam angle.