Massive Machine-Type Communications (mMTC) plays an increasingly important role in future wireless communications, and the use of a large number of low-power devices in conjunction with cell-free massive MIMO networks is a promising enabling technology. In this research, we explore activity detection for Grant-Free Random Access in massive mMTC within a cell-free Massive MIMO network employing distributed antenna arrays. Each active device sends a non-orthogonal pilot sequence to geographically clustered Access Points (APs) using the K-means algorithm. The clustered APs then relay the received signals to the Central Processing Unit (CPU) for collaborative activity detection. Simulation results demonstrate that for extensive coverage areas, the covariance-based activity detection algorithm with K-means clustered APs exhibits reduced complexity and improved activity detection efficiency while maintaining robust performance.


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    Title :

    Covariance-Based Activity Detection Algorithm in Clustered Cell-Free Massive MIMO


    Contributors:
    Ye, Haowen (author) / Lv, Ranran (author) / Sun, Kai (author) / Huang, Wei (author)


    Publication date :

    2024-05-24


    Size :

    1872730 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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