Statistical CSI Acquisition in the Nonstationary Massive MIMO Environment

Guoliang Wang, Wei Peng, Dong Li, Tao Jiang, Fumiyuki Adachi

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    This paper studies the statistical channel state information (S-CSI) acquisition problem in the nonstationary massive multiple-input multiple-output (MIMO) environment, where both the instantaneous and statistical channel states are time varying. First, we set up a hidden statistical channel state Markov model (HSCSM model). Then, the parameter of the HSCSM model is estimated through the observed sequence of received signals. Next, based on the HSCSM model and its estimated parameter, the S-CSI is obtained through a maximum a-posteriori decision process. Simulation results show that an accurate S-CSI acquisition can be achieved by the proposed approach in the nonstationary massive MIMO environment. In addition, the estimation accuracy rate of the proposed approach increases with the length of observation sequence as well as the number of antennas, where a tradeoff between them exists given a limited computing ability/storage space.

    Original languageEnglish
    Article number8344492
    Pages (from-to)7181-7190
    Number of pages10
    JournalIEEE Transactions on Vehicular Technology
    Issue number8
    Publication statusPublished - 2018 Aug


    • HSCSM-model
    • Massive MIMO
    • non-stationary
    • statistical CSI acquisition

    ASJC Scopus subject areas

    • Automotive Engineering
    • Aerospace Engineering
    • Electrical and Electronic Engineering
    • Applied Mathematics


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