Multi-User Detection Based on Structured Compressive Sensing for Uplink Grant-Free NOMA

Compared to classical orthogonal multiple access (OMA) techniques, non-orthogonal multiple access (NOMA) can support more users using the same wireless resources, which is expected to support massive connectivity for future 5G systems. Furthermore, to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this paper, by exploiting the structured user activity sparsity, we propose a structured matching pursuit (SMP) algorithm to realize both user activity and data detection in several continuous time slots. Specifically, in several continuous time slots, we can divide the active user sets into two different parts, i.e., the common active user set and the dynamic active user sets. Accordingly, the proposed SMP algorithm simultaneously detects the common active user set in all time slots at first, and then the dynamic active user sets are detected in each single time slot individually. Simulation results show that the proposed SMP algorithm can achieve much better performance than that of the conventional algorithm.