CV-3DCNN: Complex-Valued Deep Learning for CSI Prediction in FDD Massive MIMO Systems

Yibin Zhang, Jie Wang, Jinlong Sun, Bamidele Adebisi, Haris Gacanin, Guan Gui, Fumiyuki Adachi

    研究成果: Article査読

    27 被引用数 (Scopus)


    In beyond fifth-generation (B5G) era, massive multiple-input multiple-output (M-MIMO) will be a key technology to offer higher network capacities. Due to the different frequency of uplink and downlink channels in FDD systems, the channel state information (CSI) feedback from user terminal to the base station is necessary, but this reduces the spectrum efficiency. This letter proposes a deep learning based solution to predict the downlink CSI in frequency division duplex (FDD) systems, which is termed as complex-valued three dimensional convolutional neural network (CV-3DCNN). The proposed network uses a complex-valued neural network in complex domain to deal with the complex CSI matrices, and adopts three-dimensional convolution operations for feature extraction. The proposed scheme aims to make full use of the hidden information of the complex matrices of the CSI data, and to minimize information loss caused by data processing. The experimental results demonstrate that the proposed architecture can improve accuracy of the downlink CSI prediction by approximately 6 dB.

    ジャーナルIEEE Wireless Communications Letters
    出版ステータスPublished - 2021 2月

    ASJC Scopus subject areas

    • 制御およびシステム工学
    • 電子工学および電気工学


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