This paper investigates the prediction model based on deep learning for the wireless channel characteristics of massive MIMO systems in high-speed railway (HSR) scenarios. Based on the propagation graph theory, we simulate the massive MIMO channel in a HSR cutting scenario. The datasets of spatial-temporal channel characteristics, involving channel state information, Ricean K-factor, delay spread, and angle spread, are generated for the model training and testing, and two kinds of prediction problem formulations, such as single-step and multi-steps, are designed. By considering both the spatial and temporal correlation properties in HSR massive MIMO channels, a novel channel prediction model that combines the convolutional long short-term memory (CLSTM) and convolutional neural network (CNN) is proposed and called as Conv-CLSTM. The hyperparameters of Conv-CLSTM are determined by comparative experiments and autocorrelation and similarity analysis. According to the performance evaluation, it is showed that the proposed Conv-CLSTM outperforms the other deep learning and machine learning models.


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    Titel :

    Deep Learning Based Channel Prediction for Massive MIMO Systems in High-Speed Railway Scenarios


    Beteiligte:
    Xue, Chen (Autor:in) / Zhou, Tao (Autor:in) / Zhang, Haitong (Autor:in) / Liu, Liu (Autor:in) / Tao, Cheng (Autor:in)


    Erscheinungsdatum :

    01.04.2021


    Format / Umfang :

    4455316 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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