Massive multiple-input and multiple-output (MIMO), especially in the millimeter-wave (mmWave) frequency band, has been recognized as a promising technology for future wireless communications. However, most previous research in mmWave massive MIMO only uses conventional MIMO channel model without taking into account the spatial-wideband effect in mmWave massive MIMO systems, where the number of antennas is very large and the transmission bandwidth is very wide. In this paper, a novel mmWave massive MIMO channel model is introduced which embraces the spatial-wideband effect. Then, a joint parameter learning algorithm is proposed to extract different channel parameters from the received data. Furthermore, a low-complexity spatial wideband channel estimation scheme is proposed for the new channel model. Simulation results and complexity analysis show that the proposed scheme are capable of achieving a considerable reduction in complexity compared with the recently spatial-wideband channel estimation algorithm with a small performance loss. Moreover, our scheme can effectively estimate the novel channel even part of the antenna data is received.


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    Titel :

    Low-Complexity Wideband Channel Estimation for Millimeter-Wave Massive MIMO Systems via Joint Parameter Learning


    Beteiligte:
    Cheng, Long (Autor:in) / Yue, Guangrong (Autor:in) / Wang, Zhiqiang (Autor:in) / Li, Shaoqian (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    3371014 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch