Autonomous grant-free data-only transmissions greatly simplify machine type communications, but present many challenges. One of them is the compressed sensing multiple measurement vector (MMV) problem of user detection. The sensing matrix is very underdetermined, and the amount of measurement vectors is large, which affect performance and complexity, respectively. In this paper, a fast power reconstruction (FPR) algorithm is proposed. FPR simplifies MMV to single measurement vector, and no iterations is required, which reduce the complexity. The row dimension of sensing matrix is expanded, and thus the performance is improved. The simulation results validate its good performance and ultra-low computational complexity.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Fast Power Reconstruction for User Detection of Autonomous Grant-free Data-only Schemes


    Beteiligte:
    Ma, Yihua (Autor:in) / Yuan, Zhifeng (Autor:in) / Hu, Yuzhou (Autor:in) / Li, Weimin (Autor:in) / Li, Zhigang (Autor:in)


    Erscheinungsdatum :

    2020-11-01


    Format / Umfang :

    1815878 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Blind Multiple User Detection for Grant-Free MUSA without Reference Signal

    Yuan, Zhifeng / Yan, Chunlin / Yuan, Yifei et al. | IEEE | 2017


    Active User Detection of Uplink Grant-Free SCMA in Frequency Selective Channel

    Wang, Feilong / Zhang, Yuyan / Zhao, Hui et al. | IEEE | 2018


    DNN-based Active User Detection for an NB-IoT Compatible Grant Free NOMA System

    Praveen Kumar, N / Balasubramanya, Naveen Mysore | IEEE | 2022