Low-complexity detection for uplink massive MIMO SCMA systems

Abstract

This paper presents a sparse code multiple access (SCMA) system with massive antennas at the base station. This system is referred to as M-SCMA system. A spectrally-efficient and massive access next-generation wireless network is realized through massive antennas and non-orthogonal SCMA techniques. Two detection algorithms, namely, modified message passing algorithm (MMPA) and extended message passing algorithm (EMPA) are proposed to detect multiple users' symbols in M-SCMA. A deep learning (DL)-based detection scheme is also proposed for M-SCMA so as to avoid channel estimation and to lower the detection complexity. Numerical results show that the DL-based detection has similar performance as MMPA even when the channel information is not estimated explicitly. Furthermore, authors also establish the sum rate trade-off between SCMA and orthogonal multiple access in a massive antenna system. The impact of various M-SCMA parameters such as the number of antennas and the overloading factor, on the proposed DL, MMPA, and EMPA-based detection are also investigated. © 2020 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology