Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

TLDR: A structured iterative support detection algorithm is proposed by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems to jointly detect user activity and transmitted data in several continuous time slots and can achieve better performance than conventional solutions.

Abstract: Non-orthogonal multiple access (NOMA) has been regarded as one of the promising key technologies for future 5G systems. In the uplink grant-free NOMA schemes, dynamic scheduling is not required, which can significantly reduce the signaling overhead and transmission latency. However, user activity has to be detected in grant-free NOMA systems, which is challenging in practice. In this letter, by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems, we propose a low-complexity multi-user detector based on structured compressive sensing to realize joint user activity and data detection. In particular, we propose a structured iterative support detection algorithm by exploiting such structured sparsity, which is able to jointly detect user activity and transmitted data in several continuous time slots. Simulation results show that the proposed scheme can achieve better performance than conventional solutions.