用GPS+深度学习预测无人机毫米波通信波束，提升稳定性与效率
GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
- 结合GPS位置信息与深度学习，预测当前及未来最优波束方向
- 预测准确率超70%，平均功率损耗低于0.6 dB，95%情况下误差小于1 dB
- 仅需训练2~3个波束，比传统方法减少93%开销，适合高动态无人机场景
毫米波（mmWave）通信为联网无人机（UAV）提供高速数据传输，但因路径损耗大及无人机高度动态移动，波束管理仍具挑战。本文提出一种基于GPS的深度学习模型，可同时预测当前和未来最优波束，实现超过70%的Top-1预测准确率，所有预测步数下平均功率损耗低于0.6 dB。该成果得益于提出的数据集划分方法以保证标签分布均衡，结合GPS预处理提取关键位置特征，并设计端到端深度学习架构将序列位置信息映射为波束索引。模型将开销降低约93%（仅需训练2~3个波束而非32个），在95%场景中保证波束预测准确率；且94%至96%的预测结果平均功率损耗不超过1 dB。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Unmanned Aerial Vehicles (UAVs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs, which can destabilize the UAV-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications, maintaining a Top-1 prediction accuracy exceeding 70% and an average power loss below 0.6 dB across all prediction steps. These outcomes stem from a proposed data set splitting method ensuring balanced label distribution, paired with a GPS preprocessing technique that extracts key positional features, and a DL architecture that maps sequential position data to beam index predictions. The model reduces overhead by approximately 93% (requiring the training of 2 ~ 3 beams instead of 32 beams) with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB.
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