Joint User Activity and Non-Coherent Data Detection in mMTC-Enabled Massive MIMO Using Machine Learning Algorithms

Abstract

Machine-type communication (MTC) services are expected to be an integral part of future cellular systems. A key challenge of MTC is the detection of the set of active devices among a large number of devices. The sparse characteristics of MTC makes the compressed sensing (CS) approaches a promising solution to the device detection problem and CS-based techniques are shown to outperform conventional device detection approaches. However, utilizing CS-based approaches for device detection along with channel estimation and using the acquired estimates for coherent data transmission may not be the optimal approach especially for the cases where the goal is to convey a few bits of data. In this work, we propose a non-coherent transmission technique for the MTC uplink and compare its performance with coherent transmission. Furthermore, we demonstrate that it is possible to obtain more accurate channel state information by combining the conventional estimators with CS-based techniques.