Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs simultaneously in a fast and effective way. Specifically, we construct a beam alignment architecture that utilizes only angular information. To obtain the angle information, the currently hottest seq2seq model - the Transformer - is introduced to offline learn the relationship between UE geographic location and the corresponding optimal beam direction. Based on the powerful machine learning model, the location-angle mapping function, i.e., the angle map, can be built. As long as the location information of UEs is available, the angle map can make the acquisition of beam alignment angles effortless. In the simulation, we utilize a ray-tracing-based dataset to verify the performance of the proposed scheme. It is demonstrated that the proposed scheme can achieve high-precision beam alignment and remarkable system performance without any beam scanning.

翻译：近年来，可重构智能表面（RIS）被广泛应用于增强毫米波通信系统性能，这使得波束对准更具挑战性。为确保高效通信，本文提出了一种新颖的基于智能角度图的波束对准方案，能够以快速有效的方式同时为普通用户设备（UE）和RIS辅助用户设备实现波束对准。具体而言，我们构建了一种仅利用角度信息的波束对准架构。为获取角度信息，我们引入了当前最热门的序列到序列模型——Transformer，以离线学习用户设备地理位置与对应最优波束方向之间的关联关系。基于这一强大的机器学习模型，可以构建位置-角度映射函数，即角度图。只要能够获取用户设备的位置信息，角度图即可轻松实现波束对准角度的获取。在仿真中，我们利用基于射线追踪的数据集验证了所提方案的性能。结果表明，所提方案无需任何波束扫描即可实现高精度波束对准和卓越的系统性能。