This paper presents the first machine learning based real-world demonstration
for radar-aided beam prediction in a practical vehicular communication
scenario. Leveraging radar sensory data at the communication terminals provides
important awareness about the transmitter/receiver locations and the
surrounding environment. This awareness could be utilized to reduce or even
eliminate the beam training overhead in millimeter wave (mmWave) and
sub-terahertz (THz) MIMO communication systems, which enables a wide range of
highly-mobile low-latency applications. In this paper, we develop deep learning
based radar-aided beam prediction approaches for mmWave/sub-THz systems. The
developed solutions leverage domain knowledge for radar signal processing to
extract the relevant features fed to the learning models. This optimizes their
performance, complexity, and inference time. The proposed radar-aided beam
prediction solutions are evaluated using the large-scale real-world dataset
DeepSense 6G, which comprises co-existing mmWave beam training and radar
measurements. In addition to completely eliminating the radar/communication
calibration overhead, the experimental results showed that the proposed
algorithms are able to achieve around $90\%$ top-5 beam prediction accuracy
while saving $93\%$ of the beam training overhead. This highlights a promising
direction for addressing the beam management overhead challenges in mmWave/THz
communication systems.

本文介绍了第一次使用基于机器学习的雷达辅助波束预测在实际车联网通信场景中的应用，该应用可利用雷达传感器数据提供的关于收发位置和周边环境的信息来降低或消除毫米波和亚太赫兹的 MIMO 通信系统中波束训练的开销，实现低延迟高流动性应用。本文提出了利用雷达信号处理的领域知识来提取相关特征并将其馈入学习模型的深度学习雷达辅助波束预测方法，并使用了包含毫米波波束训练和雷达测量的大规模真实数据集 Deepsense 6G 来评估所提出的算法，结果显示所提出的算法能够在完全消除雷达 / 通信校准开销的同时达到约 90% 的前 5 个波束预测准确率，节省了 93% 的波束训练开销，这为解决毫米波 / 亚太赫兹通信系统中波束管理开销挑战开辟了一个有希望的方向。