论文标题
迈向现实世界6G无人机通信：位置和相机辅助光束预测
Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction
论文作者
论文摘要
毫米波（MMWave）和Terahertz（THZ）通信系统通常部署大型天线阵列，以确保足够的接收信号功率。但是，与这些阵列相关的横梁训练开销使这些系统很难支持诸如无人机通信之类的高管应用。为了克服这一挑战，本文提出了一种基于机器学习的方法，该方法利用了其他感觉数据，例如视觉和位置数据，以快速准确的MMWAVE/THZ BEAM预测。在现实世界中的多模式MMWave无人机通信数据集上评估了开发的框架，该数据集由共存的摄像头，实用的GPS和MMWave Beam训练数据进行评估。提出的感应辅助溶液可实现86.32％的TOP-1梁预测精度，接近100％的前3名和前5个精度，同时大大降低了梁训练开销。这突出了一个有前途的解决方案，用于实现高度移动的6G无人机通信。
Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power. The beam training overhead associated with these arrays, however, make it hard for these systems to support highly-mobile applications such as drone communication. To overcome this challenge, this paper proposes a machine learning-based approach that leverages additional sensory data, such as visual and positional data, for fast and accurate mmWave/THz beam prediction. The developed framework is evaluated on a real-world multi-modal mmWave drone communication dataset comprising of co-existing camera, practical GPS, and mmWave beam training data. The proposed sensing-aided solution achieves a top-1 beam prediction accuracy of 86.32% and close to 100% top-3 and top-5 accuracies, while considerably reducing the beam training overhead. This highlights a promising solution for enabling highly mobile 6G drone communications.
