A Study on Performance Improvement by CRC-Aided GaBP for Large-Scale SCMA Detection

Abstract

Sparse code multiple access (SCMA) has attracted attention as a new non-orthogonal multiple access (NOMA) scheme that is suitable for device-to-device communications. In general, the NOMA signal detection becomes complicated as the number of users increases. Although the SCMA is a kind of low-density spreading, it requires an interference canceller. In this paper, Gaussian belief propagation (GaBP) is applied to detecting the SCMA signals because the complexity order of GaBP is the square of the graph size. In addition, we apply CRCaided stopping of iterative processes to GaBP. The simulation results show that the CRC-aided GaBP provides much lower FER performance and that the number of iterations is highly reduced.