Classification and comparison of massive MIMO propagation channel models

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Feng, Rui and Wang, Cheng-Xiang and Huang, Jie and Gao, Xiqi and Salous, Sana and Haas, Harald (2022) Classification and comparison of massive MIMO propagation channel models. IEEE Internet of Things Journal, 9 (23). pp. 23452-23471. ISSN 2327-4662 (https://doi.org/10.1109/jiot.2022.3198690)

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Abstract

Considering great benefits brought by massive multiple-input-multiple-output (MIMO) technologies in the Internet of Things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical versus analytical method and deterministic versus stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical nonpredictive channel models, which further clarify and combine the deterministic versus stochastic and physical versus analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical nonpredictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include the correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam-domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given.

  • Item type:Article
    ID code:82288
    Dates:
    Date
    Event
    1 December 2022
    Published
    16 August 2022
    Published Online
    17 July 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:09 Sep 2022 12:46
    Last modified:12 Dec 2024 13:44
    URI:https://strathprints.strath.ac.uk/id/eprint/82288
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