Real-Time Beam Tracking Algorithm for UAVs in Millimeter-wave Networks with Adaptive Beamwidth Adjustment

Maintaining stable and efficient communication links for high-speed unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) communication systems remains a challenge due to beam misalignment caused by UAV mobility. Existing beam-tracking methods often struggle to provide accurate tracking under dynamic conditions, leading to frequent communication disruptions. To address this issue, we propose an enhanced Interactive Multiple Model (IMM) algorithm integrated with a Random Forest Model (RF-EIMM) to improve the accuracy and robustness of UAV trajectory predictions across diverse motion patterns. Furthermore, we propose an adaptive beamwidth optimization strategy that dynamically adjusts the beamwidth in real time, reducing the beam switching frequency, and minimizing the power consumption of the antenna array. Experimental results demonstrate that our approach significantly improves beam alignment accuracy, mitigates misalignment caused by UAV mobility, and outperforms existing methods in terms of spectral efficiency and beamforming gain.