  Unsourced random access is a novel communication paradigm designed for
handling a large number of uncoordinated users that sporadically transmit very
short messages. Under this model, coded compressed sensing (CCS) has emerged as
a low-complexity scheme that exhibits good error performance. Yet, one of the
challenges faced by CCS pertains to disentangling a large number of codewords
present on a single factor graph. To mitigate this issue, this article
introduces a modified CCS scheme whereby active devices stochastically
partition themselves into groups that utilize separate sampling matrices with
low cross-coherence for message transmission. At the receiver, ideas from the
field of compressed demixing are employed for support recovery, and separate
factor graphs are created for message disambiguation in each cluster. This
reduces the number of active users on a factor graph, which improves
performance significantly in typical scenarios. Indeed, coded demixing reduces
the probability of error as the number of groups increases, up to a point.
Findings are supported with numerical simulations.
