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Massive machine-type communication (mMTC) is a newly introduced servicecategory in 5G wireless communication systems to support a variety ofInternet-of-Things (IoT) applications. In recovering sparsely representedmulti-user vectors, compressed sensing based multi-user detection (CS-MUD) canbe used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonalmultiple access (NOMA) environments. In CS-MUD, active user detection (AUD) andchannel estimation (CE) should be performed before data detection. In thispaper, 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 computationallyintractable probability distribution to an easily tractable distribution. Theproposed technique finds the best approximation of the posterior distributionof the sparse channel vector. Using the approximate distribution, AUD and CEare jointly performed. We show by numerical simulations that the proposedtechnique substantially enhances AUD and CE performances over competingalgorithms.

EP-based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications