  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.
