Research on MIMO beam weight planning method for low-altitude UAV communication in urban occluded environments

Abstract

Driven by the rapid development of the low-altitude economy, unmanned aerial vehicles (UAVs) urgently require high reliability, low-latency, and continuous coverage in complex urban environments. However, conventional 5G networks employ fixed two-dimensional (2D) beamforming strategies optimized for ground users, resulting in severe signal blockage, frequent handover failures, and sharp rate degradation when UAVs fly at 50–500 m altitude. To address these challenges, this paper proposes a three-dimensional (3D) intelligent beam weight planning framework that fuses 5G-Aintegrated sensing and communication (ISAC) and Massive MIMO beamforming. The proposed solution consists of three core modules: multi-cell route segmentation, occlusion-aware region classification, and sub-beam power adaptive optimization. A 3D ray-tracing method is applied to quantify building occlusion loss, and a linear weighting model is designed to optimize azimuth, elevation, and transmit power. Field tests in urban dense building scenarios demonstrate that the proposed method reduces the coverage blind spot rate from 28.5% to 11.8%, decreases the communication interruption rate from 14.2% to 2.7%, and improves spectrum efficiency by 48.5%. Meanwhile, base station deployment efficiency is enhanced by 60%. This method provides a low-cost, high-performance solution for low-altitude UAV applications and exhibits strong practical value for large-scale commercialization.