Low-Altitude UAV Position Prediction-Assisted Near-Field Adaptive Beamwidth Control for XL-MIMO Systems

Recently, unmanned aerial vehicles (UAVs) are crucial in the low-altitude economy due to their high mobility and operational efficiency, which enable rapid and flexible operations in various applications. To support the flight control and management of low-altitude UAVs, ground base stations need to ensure communication quality that is high-capacity, ultra-low latency, and highly reliable. Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, capable of forming high-gain directional beams, are highly suited for supporting the communication requirements of UAVs in the low-altitude economy. However, the communication signal may quickly deviate from the main lobe of the beam due to the mobility of UAVs, resulting in beam misalignment and significant degradation in communication quality. To address this problem, this paper first analyzes the near-field beam pattern of XL-MIMO system serving low-altitude UAVs and then decomposes phase of beam pattern into linear and nonlinear components to derive the angular and distance half-power beamwidth in the near field. Then, the prediction error is used to determine the number of activated antennas for dynamic beamwidth adjustment based on UAV position prediction. In addition, a near-field adaptive beamwidth control-aided tracking (NF-ABCT) algorithm is proposed to improve the effectiveness of beam tracking. Finally, simulation results demonstrate that the proposed NF-ABCT achieves a higher signal-to-noise ratio compared to the existing fixed beam scheme and channel estimation methods, while maintaining beam traking robustness and stability in both near-field and far-field.