  Massive machine-type communication (MTC) with sporadically transmitted small
packets and low data rate requires new designs on the PHY and MAC layer with
light transmission overhead. Compressive sensing based multiuser detection
(CS-MUD) is designed to detect active users through random access with low
overhead by exploiting sparsity, i.e., the nature of sporadic transmissions in
MTC. However, the high computational complexity of conventional sparse
reconstruction algorithms prohibits the implementation of CS-MUD in real
communication systems. To overcome this drawback, in this paper, we propose a
fast Deep learning based approach for CS-MUD in massive MTC systems. In
particular, a novel block restrictive activation nonlinear unit, is proposed to
capture the block sparse structure in wide-band wireless communication systems
(or multi-antenna systems). Our simulation results show that the proposed
approach outperforms various existing algorithms for CS-MUD and allows for
ten-fold decrease of the computing time.
