Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Non-Orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) has become one of the promising key technologies for future 5G wireless communications to improve spectral efficiency and support massive connectivity. However, in the uplink grant-free NOMA system, the current near-optimal multi-user detection (MUD) based on message passing algorithm (MPA) assumes that the user activity information is exactly known at the receiver, which is impractical yet challenging due to anyone of massive users can randomly enter or leave the system. In this paper, inspired by the observation of user sparsity, we jointly use compressive sensing (CS) and MPA to propose a CS-MPA detector to realize both user activity and data detection for uplink grant-free NOMA. Specifically, the MUD problem is firstly formulated under CS framework by exploiting user sparsity, and then user activity can be detected by sparse signal recovery algorithms in CS. Then, MPA can be performed to reliably detect active users' data. It is shown that the proposed CS-MPA detector with affordable complexity not only outperforms the conventional MPA detector without user activity information, but also achieves very close performance to the genie-knowledge MPA detector with exact knowledge of user activity, especially when the signal-to-noise ratio (SNR) is high.