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

Yuan Xu, Chongwen Huang, Li Wei, Zhaohui Yang, Xiaoming Chen, Zhaoyang Zhang, Chau Yuen, Merouane Debbah

    Research output: Contribution to journalArticlepeer-review

    Abstract

    In this letter, 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 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 the existing beam training techniques.

    Original languageBritish English
    Pages (from-to)2030-2034
    Number of pages5
    JournalIEEE Wireless Communications Letters
    Volume13
    Issue number8
    DOIs
    StatePublished - 2024

    Keywords

    • Beam training
    • hashing codebook
    • multi-arm beam
    • multi-round voting mechanism
    • reconfigurable intelligent surface
    • soft decision

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