Effective massive machine-type communication (mMTC) with short packet transmissions is essential to realize a fully connected Internet of Things (IoT). On the other hand, sparse vector coding (SVC) techniques have recently been proposed to support short packet communication (SPC) systems. The principle of SVC is to transmit the information as a sparse vector and then use a sparse recovery algorithm for signal decoding. This paper applies two new sparse recovery algorithms to SVC-SPC; namely, Compressive Sampling Matching Pursuit (CoSaMP) and Stagewise Orthogonal Matching Pursuit (St-OMP) and compares their performance with two of the previously proposed recovery algorithms for SVC-SPC including the Multipath Matching Pursuit (MMP) and Orthogonal Matching Pursuit (OMP) algorithms. Simulation and numerical results of the recovery errors, recovery times, covariance and block error rates are presented for the different recovery algorithms. We show the MMP to be the more effective algorithm in SPC with low recovery error, while CoSaMP and OMP consume less operational time.


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    Titel :

    Sparse Recovery Algorithms Implementations for Short Packet Communications


    Beteiligte:


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    388730 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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