Coded Demixing for Unsourced Random Access

Jamison R. Ebert*, Vamsi K. Amalladinne, Stefano Rini, Jean Francois Chamberland, Krishna R. Narayanan


研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)


Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem, coded compressed sensing (CCS) has emerged as a pragmatic signal processing tool that, when applied to URA, offers good performance at low complexity. While CCS is effective at recovering a signal that is sparse with respect to a single basis, it is unable to jointly recover signals that are sparse with respect to separate bases. In this article, the CCS framework is extended to the demixing setting, yielding a novel technique called coded demixing. A generalized framework for coded demixing is presented and a low-complexity recovery algorithm based on approximate message passing (AMP) is developed. Coded demixing is applied to heterogeneous multi-class URA networks and traditional single-class networks. Its performance is analyzed and numerical simulations are presented to highlight the benefits of coded demixing.

頁(從 - 到)2972-2984
期刊IEEE Transactions on Signal Processing
出版狀態Published - 2022


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