CS-Based Near-Optimal MUD for Uplink Grant-Free NOMA

Abstract

In order to furnish connectivity to an incredible number of devices and to satisfy the capacity demands, non-orthogonal multiple access (NOMA) is considered as a hopeful solution for future 5G. Moreover, grant-free transmission is substantially expected in the uplink NOMA to minimize the latency time and detection overhead. However, multi-user detection (MUD) without user-activity information in uplink grant-free NOMA is hard in practice. In this paper, by benefit from the user-activity sparsity existing in uplink grant-free NOMA, the multipath matching pursuit (MMP) is used for user-activity detection, and then message passing algorithm (MPA) can be efficiently applied for active users’ data detection. Simulation results show that the proposed MMP-MAP MUD with acceptable complexity can achieve better performance than conventional solutions. Also, the MMP-MAP detector reaches the performance obtained by the MPA detector with perfect user-activity information once the level of signal-to-noise ratio exceed 11 dB.