Multi-Modal Environment Semantics Information Aided UAV Beam Alignment

In 6 G directional communications, high-frequency millimeter-wave (mmWave) and terahertz (THz) signals suffer from severe propagation loss and are highly susceptible to blockage, while the high mobility of the unmanned aerial vehicle (UAV) further complicates link establishment. To address these challenges, this paper proposes a novel UAV beam alignment scheme that leverages multi-modal environment semantics information to improve beam alignment accuracy by integrating multi-modal semantics into highly dynamic UAV environments. Specifically, a dual-path feature extraction module that acquires visual semantics via target detection and positional semantics through GPS/INS fusion is presented. A neural network based on bidirectional cross-attention mechanism is designed to effectively fuse multi-modal environment semantics information to output the optimal beam. Simulation results demonstrate significant improvements in beam alignment accuracy through multi-modal fusion. Compared to existing vision-only and position-only schemes, the proposed scheme achieves increases in Top-1 accuracy of 16.25% and 74.66%, respectively.