In this paper, we derive the Bayes-Optimal estimator based on approximate message passing (AMP) algorithm in massive multiple-input multiple-output (MIMO) systems, which requires statistical channel state information (CSI). According to the analysis of channel model in beam domain, the covariance matrix is derived for CSI acquisition. With the aid of statistical CSI, the convergence of the proposed algorithm has significant improvement in comparison with which use the expectation-maximization (EM) algorithm to fit the statistical CSI. Simulations show great mean squared error (MSE) performance that approximates the Minimum Mean Square Error (MMSE) estimator, and better convergence performance than other AMP algorithm can be achieved. Besides, the results prove that performance of the random pilot in this algorithm is close to that of the orthogonal pilot based on Zadoff-Chu sequences.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bayesian Channel Estimation for Massive MIMO Communications


    Beteiligte:
    Zhu, Chengzhi (Autor:in) / Zheng, Zhitan (Autor:in) / Jiang, Bin (Autor:in) / Zhong, Wen (Autor:in) / Gao, Xiqi (Autor:in)


    Erscheinungsdatum :

    2016-05-01


    Format / Umfang :

    217875 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Channel Estimation for FDD Massive MIMO OFDM Systems

    Hu, Die / He, Lianghua | IEEE | 2017


    mmWave massive MIMO vehicular communications

    Cheng, Xiang / Gao, Shijian / Yang, Liuqing | TIBKAT | 2023



    Machine Learning-Assisted Channel Estimation in Massive MIMO Receiver

    Yarotsky, Dmitry / Ivanov, Andrey / Bychkov, Roman et al. | IEEE | 2021


    Massive MIMO air-to-ground communications

    Zhang, Chao / Xiao, Jialuo / Zhang, Yaxin | IEEE | 2014