This paper investigates the problem of joint active user detection (AUD) and channel estimation (CE) for grant-free random access in a cell-free massive multi-input multi-output (MIMO) system. Due to the sporadic activation of users and the channel block sparsity caused by large-scale fading in the cell-free system, the effective channel matrix exhibits a dual sparsity property. Considering this dual sparsity, joint AUD and CE are formulated as a sparse signal reconstruction problem based on compressed sensing. A ‘three-choice-one’ prior assumption is employed to extract the block sparsity in the antenna dimension. Then a variational Bayesian inference-based algorithm is proposed, and the simulation results validate the reliability of the proposed algorithm.


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    Title :

    Variational Bayesian Inference-Based Joint Active User Detection and Channel Estimation in Cell-Free Massive MIMO


    Contributors:
    Liu, Junhui (author) / Zhu, Shihao (author) / Zhao, Ming (author) / Zhou, Wuyang (author)


    Publication date :

    2024-06-24


    Size :

    1033749 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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