LAUSR

Exploiting the sparse user activity induced by sporadic transmission, compressed sensing (CS) has been widely applied in multiple-input multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) for efficient multiuser detection. However, most of the existing detection schemes in MIMO enabled NOMA systems focus mainly on the spatial structure in user activity induced by multiantenna reception, while the temporal correlation in user activity has rarely been incorporated for further performance improvement. To address this issue, we propose a novel Multiuser Detection framework in MIMO enabled NOMA (MDMN), which explicitly integrates the temporal correlation in user activity into the detection process to improve the user detection accuracy. Specifically, we first formulate multiuser detection with multiantenna reception in MDMN as a block CS problem, by exploiting the spatial correlation in user activity. Furthermore, through incorporating the temporal correlation in active user sets into block CS, an adaptive CS algorithm based on subspace pursuit is developed for MDMN, named spatial-temporal correlation enhanced adaptive subspace pursuit (STASP). In particular, STASP does not require any prior knowledge of the user sparsity, as the cross validation designed in STASP can properly terminate the algorithm. The superior performance of the proposed MDMN framework and the corresponding algorithms is corroborated by extensive simulation results.