This paper proposes a Machine Learning (ML) algorithm for hybrid beamforming in millimeter-wave wireless systems with multiple users. The time-varying nature of the wireless channels is taken into account when training the ML agent, which identifies the most convenient hybrid beamforming matrix with the aid of an algorithm that keeps the amount of signaling information low, avoids sudden changes in the analog beamformers radiation patterns when scheduling different users (flashlight interference), and simplifies the hybrid beamformer update decisions by adjusting the phases of specific analog beamforming vectors. The proposed hybrid beamforming algorithm relies on Deep Reinforcement Learning (DRL), which represents a practical approach to embed the online adaptation feature of the hybrid beamforming matrix into the channel states of continuous nature in which the multiuser MIMO system can be. Achievable data rate curves are used to analyze performance results, which validate the advantages of DRL algorithms with respect to solutions relying on conventional/deterministic optimization tools.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems




    Publication date :

    2021-04-01


    Size :

    1958372 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    User-Directed Analog Beamforming for Multiuser Millimeter-Wave Hybrid Array Systems

    Zhang, J. Andrew / Li, Hang / Huang, Xiaojing et al. | IEEE | 2017


    Beamforming in Multi-User MISO Cellular Networks with Deep Reinforcement Learning

    Chen, Hongchao / Zheng, Zhe / Liang, Xiaohui et al. | IEEE | 2021


    Hybrid Beamforming for Broadband Millimeter Wave Massive MIMO Systems

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


    Hybrid Beamforming Based on Dictionary Learning for Millimeter Wave MIMO System

    Zhu, Li / Zhu, Jiang / Wang, Shilian et al. | IEEE | 2019


    Low-Complexity Hybrid Digital-to-Analog Beamforming for Millimeter-Wave Systems with High User Density

    Nair, Manish / Ahmed, Qasim Zeeshan / Wang, Junyuan et al. | IEEE | 2017