The sixth-generation (6G) wireless communication networks are anticipated to combine terrestrial, aerial, and marine communications into a dependable, fast network that could handle many devices with ultra-low latency requirements. Reconfigurable intelligent surfaces (RIS)-aided millimeter-wave massive MIMO communication systems could increase wireless link quality by providing passive beamforming gain via low-cost reflecting components. On the other hand, the power consumption and cost could be reduced much by applying a hybrid precoding architecture, which combines digital and analog precoding modules. However, using RIS to solve the problem of hybrid precoding is difficult because how to Figure out reflecting coefficients without beam training overhead or large channel estimation is a challenging issue. In this work, we propose a novel hybrid precoding architecture based on geometric mean decomposition (GMD) and jointly consider the design of the Long ShortTerm Memory (LSTM) autoencoder scheme. That is, we adopt GMD for diminishing the computational complexity and enhancing the hybrid precoding performance. Since the encoder-decoder design of autoencoder serves as a dimensionality reduction strategy, the LSTM autoencoder are able to capture the temporal and spatial distribution of the sequential data by using the LSTM models sequenhal and feature extraction capabilities. Numerical results show that our proposed algorithm could significantly improve the RIS-aided millimeter-wave MIMO communication systems performance compared with previous works.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    On LSTM Autoencoder-Based Hybrid Precoding for Reconfigurable Intelligent Surface-Aided Multiuser Millimeter-Wave Massive MIMO 6G Systems


    Contributors:


    Publication date :

    2022-06-01


    Size :

    649172 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems with Low-Resolution DACs

    Guo, Yajing / Zhang, Yunliang / Chen, Shuaifei et al. | IEEE | 2020


    One-Bit Hybrid Precoding for Wideband Millimeter-Wave Massive MIMO Systems

    Mir, Talha / Siddiqi, Zain / Mir, Usama et al. | IEEE | 2019



    ADMM for Hybrid Precoding of Relay in Millimeter-Wave Massive MIMO System

    Xu, Wenyuan / Wang, Yongchao / Xue, Xuan | IEEE | 2018


    Hybrid Beamforming for Broadband Millimeter Wave Massive MIMO Systems

    Chen, Rui / Xu, Hui / Li, Changle et al. | IEEE | 2018