Performance Analysis of MIMO-NOMA Iterative Receivers for Massive Connectivity

The Fifth Generation (5G) of wireless networks introduced support to Machine-Type Communications (MTC), which is the wireless connectivity solution for Internet of Things (IoT) applications. MTC is split into two different categories: massive MTC (mMTC) and critical MTC (cMTC). Current 5G standards and technologies are not capable of fully satisfying the requirements of both mMTC and cMTC use cases, thus industry and academia have already started developing solutions for MTC in beyond-5G and 6G networks. In some mMTC use cases, receivers might not be equipped with a large number of antennas owing to cost, size or power limitations, thus the number of active devices in a time slot may surpass the number of antennas. Due to the limited spatial multiplexing capabilities, only multi-antenna techniques are not enough to provide connectivity to a massive number of devices in such scenarios. In this paper, we propose and evaluate the performance of iterative linear receivers that can address this issue. By combining Multiple-Input Multiple-Output (MIMO) techniques with Non-Orthogonal Multiple Access (NOMA) exploiting Successive Interference Cancellation (SIC) or Parallel Interference Cancellation (PIC) decoding, the proposed novel receivers are capable of performing dynamic ordering SIC/PIC decoding of multiple overlapping signals even when the number of active devices surpasses that of receive antennas. The performance of the receivers is studied in terms of outage probability and computational complexity. Simulation results show that, among all the receivers studied in this paper, the PIC-based Minimum Mean Square Error (MMSE) receiver presents the best performance while at the same time reducing the number of complex signal operations such as matrix inversions.