Low-Complexity Beam Training for Multi-RIS-Assisted Multi-User Communications

TL;DR

This work tackles beam training in multi-RIS mmWave systems with multiple users, where conventional methods suffer from high complexity and limited accuracy. It introduces hashing multi-arm beams (HMB) to reduce training complexity to $O(B\\log N)$ while maintaining high identification accuracy, enabling simultaneous training across multiple RISs and users via independent hash functions and a soft-decision, multi-round voting workflow. The key contributions are a hashing-based codebook generation, a two-phase training process (scanning and identification), and a demultiplexing mechanism that leverages signal strengths to assign RIS-user directions, achieving significant accuracy gains (>=20%) and logarithmic overhead. The approach provides scalable, practical beam training for expansive RIS deployments in uplink multi-user mmWave networks, with potential for real-world deployment where rapid, concurrent RIS alignment is required.

Abstract

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.

Figures (5)

• Figure 1: The considered multi-RIS-assisted multi-user mmWave communication system, consisting of a $N_A$-antenna BS, $K$ single-antenna mobile users, and $I$ distributed RISs.
• Figure 2: Illustration of the proposed multi-arm beams generating method when $N_1=16$, $N_2=1$, $B=4$.
• Figure 3: Proposed multi-RISs voting mechanism with $N=16$, $B=4$, $R=4$.
• Figure 4: Beam identification accuracy versus the SNR for different training methods when $N=32$.
• Figure 5: Beam training overhead.