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.
Abstract-In a wireless multiple-input multiple-output (MIMO) system, transmit antenna selection is an effective means of achieving good performance with low complexity. We consider spatial multiplexing with linear receivers, and equal power and equal rate allocation over different selected transmit antennas in order to reduce feedback overhead. Under these constraints, we address the problem of statistical multimode transmit antenna subset selection to improve the capacity of spatially correlated MIMO fading channels. In particular, we first derive an analytical closed-form expression for the expectation of the lower bound on the capacity using the smallest eigenvalue distribution of a Wishart matrix. Then, we propose a transmit antenna subset selection criterion of maximizing this average lower-bound capacity.
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