  Massive machine-type communication (mMTC) is a newly introduced service
category in 5G wireless communication systems to support a variety of
Internet-of-Things (IoT) applications. In recovering sparsely represented
multi-user vectors, compressed sensing based multi-user detection (CS-MUD) can
be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal
multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and
channel estimation (CE) should be performed before data detection. In this
paper, we propose the expectation propagation based joint AUD and CE
(EP-AUD/CE) technique for mMTC networks. The expectation propagation (EP)
algorithm is a Bayesian framework that approximates a computationally
intractable probability distribution to an easily tractable distribution. The
proposed technique finds the best approximation of the posterior distribution
of the sparse channel vector. Using the approximate distribution, AUD and CE
are jointly performed. We show by numerical simulations that the proposed
technique substantially enhances AUD and CE performances over competing
algorithms.
