Preprint Article Version 1 This version is not peer-reviewed

On the Application of Massive MIMO Systems to Machine Type Communications

Version 1 : Received: 7 December 2018 / Approved: 10 December 2018 / Online: 10 December 2018 (12:38:34 CET)

How to cite: de Figueiredo, F.A.P.; Cardoso, F.A.C.M..; Moerman, I..; Fraidenraichz, G.. On the Application of Massive MIMO Systems to Machine Type Communications. Preprints 2018, 2018120100 (doi: 10.20944/preprints201812.0100.v1). de Figueiredo, F.A.P.; Cardoso, F.A.C.M..; Moerman, I..; Fraidenraichz, G.. On the Application of Massive MIMO Systems to Machine Type Communications. Preprints 2018, 2018120100 (doi: 10.20944/preprints201812.0100.v1).

Abstract

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

Subject Areas

Large-scale antenna systems, 5G networks, machine type communications, channel estimation, linear detection

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