  Grant-free non-orthogonal multiple access (NOMA) scheme is considered as a
promising candidate for the enabling of massive connectivity and reduced
signalling overhead for Internet of Things (IoT) applications in massive
machine-type communication (mMTC) networks. Exploiting the inherent nature of
sporadic transmissions in the grant-free NOMA systems, compressed sensing based
multiuser detection (CS-MUD) has been deemed as a powerful solution to user
activity detection (UAD) and data detection (DD). In this paper, block
coordinate descend (BCD) method is employed in CS-MUD to reduce the
computational complexity. We propose two modified BCD based algorithms, called
enhanced BCD (EBCD) and complexity reduction enhanced BCD (CR-EBCD),
respectively. To be specific, by incorporating a novel candidate set pruning
mechanism into the original BCD framework, our proposed EBCD algorithm achieves
remarkable CS-MUD performance improvement. In addition, the proposed CR-EBCD
algorithm further ameliorates the proposed EBCD by eliminating the redundant
matrix multiplications during the iteration process. As a consequence, compared
with the proposed EBCD algorithm, our proposed CR-EBCD algorithm enjoys two
orders of magnitude complexity saving without any CS-MUD performance
degradation, rendering it a viable solution for future mMTC scenarios.
Extensive simulation results demonstrate the bound-approaching performance as
well as ultra-low computational complexity.
