论文标题
MMWave通信系统中的层次光束对齐的深度学习
Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems
论文作者
论文摘要
快速和精确的光束对齐对于支持毫米波（MMWave）通信系统中的高质量数据传输至关重要。在这项工作中，我们提出了一种基于深度学习的新型层次光束对准方法，该方法学习了两层探测代码簿（PC），并使用其测量值以粗到最细的搜索方式预测最佳光束。具体而言，提出的方法首先使用Tier-1 PC执行粗糙的通道测量，然后选择一个层-2 PC进行良好的通道测量，并最终基于粗糙和精细测量来预测最佳光束。拟议的深神经网络（DNN）体系结构分为两个步骤。首先，对Tier-1 PC和Tier-2 PC选择器进行了联合训练。之后，所有层2 PC与最佳束预测变量共同训练。学到的分层PC可以捕获传播环境的特征。基于现实的射线追踪数据集的数值结果表明，该方法在对齐精度和扫地开销中都优于最先进的束对准方法。
Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.
