Low‐complexity detection for uplink massive MIMO SCMA systems

Abstract

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.