Accurate channel state information (CSI) is critical for achieving high performance in massive multiple input multiple output (MIMO) systems. While existing deep learning (DL) based methods have achieved notable success for CSI feedback in the frequency division duplex (FDD) mode, they typically learn one set of neural network (NN) parameters for all CSI. However, only one set of parameters restricts the representation power of the NN, resulting in the limited performance. In addition, the channel estimation error is usually considered with discrete levels among the researches of CSI feedback, which limits the performance when channel estimation errors are successive. To address these issues, we propose a model-driven DL method with sample-relevant dynamic parameters using hyper-networks and unfolding. The proposed method can generate the parameters of the task network distinctly for each CSI by a hyper-network, which improves the representation power and recovery performance of the task network. Additionally, instead of assuming each CSI has the same level of channel estimation error, the proposed method automatically adjusts task network parameters to account for different levels of channel estimation error, resulting in significant performance gains. The numerical experiments demonstrate the superiority of the proposed method in terms of performance and robustness.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Hyper-Network-Aided Approach for ISTA-based CSI Feedback in Massive MIMO systems


    Contributors:
    Zou, Yafei (author) / Hu, Zhengyang (author) / Zhang, Yiqing (author) / Xue, Jiang (author)


    Publication date :

    2023-10-10


    Size :

    1305485 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems

    Gao, Junjie / Bouazizi, Mondher / Ohtsuki, Tomoaki et al. | IEEE | 2022


    Data-Aided LS Channel Estimation in Massive MIMO Turbo-Receiver

    Osinsky, Alexander / Ivanov, Andrey / Lakontsev, Dmitry et al. | IEEE | 2020



    Automatic Neural Network Design of Scene-customization for Massive MIMO CSI Feedback

    Li, Xiangyi / Guo, Jiajia / Wen, Chao Kai et al. | IEEE | 2023