In this paper, we investigate the beam training problem in the multi-user millimeter wave (mmWave) communication system, where multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the coverage and the achievable rate. However, existing beam training techniques in mmWave systems suffer from the high complexity (i.e., exponential order) and low identification accuracy. To address these problems, we propose a novel hashing multi-arm beam (HMB) training scheme that reduces the training complexity to the logarithmic order with the high accuracy. Specifically, we first design a generation mechanism for HMB codebooks. Then, we propose a demultiplexing algorithm based on the soft decision to distinguish signals from different RIS reflective links. Finally, we utilize a multi-round voting mechanism to align the beams. Simulation results show that the proposed HMB training scheme enables simultaneous training for multiple RISs and multiple users, and reduces the beam training overhead to the logarithmic level. Moreover, it also shows that our proposed scheme can significantly improve the identification accuracy by at least 20% compared to existing beam training techniques.

翻译：本文研究了多用户毫米波通信系统中的波束训练问题，其中部署了多个可重构智能超表面（RIS）以提升覆盖范围和可达速率。然而，现有毫米波系统中的波束训练技术存在复杂度高（指数级）和识别精度低的问题。为应对这些挑战，我们提出了一种新颖的哈希多臂波束（HMB）训练方案，将训练复杂度降至对数级，同时保持高精度。具体而言，我们首先设计了HMB码本的生成机制；随后，提出一种基于软判决的解复用算法，以区分来自不同RIS反射链路的信号；最后，利用多轮投票机制实现波束对齐。仿真结果表明，所提HMB训练方案能够对多个RIS和多个用户进行并行训练，并将波束训练开销降至对数级。此外，与现有波束训练技术相比，该方案在识别精度上至少提升20%。