Temporal Correlation Enhanced Multiuser Detection for Uplink Grant-Free NOMA

Compressed sensing (CS) has been identified as a good candidate for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the inherent sparsity of user activity. However, most of the existing CS-based user detection schemes do not fully utilize the temporal correlation of user activity in NOMA and rely heavily on the unrealistic assumption that the number of active users is known in advance. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme to achieve efficient and pragmatic multiuser detection. First, using 1-bit memory to piggyback the information on whether the active users still have data to transmit, the base station can realize that the active users in the current time slot will turn to be silent or remain active. Then, to make explicit use of the temporal correlation of active user sets, a cross validation based adaptive subspace pursuit (CVASP) algorithm is developed by utilizing the reported information on prior active users. The proposed CVASP is a highly practical algorithm that does not require any prior knowledge of the number of active users or the noise level, as the cross validation technique could properly determine the stopping condition. Extensive simulation results demonstrate that the proposed mechanism could achieve almost the same performance as compared to the existing state of art CS-based multiuser detection algorithms while eliminating the need for any prior knowledge.

Automated summary

Compressed sensing (CS) is a good option for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the sparsity of user activity. Existing CS-based user detection schemes do not fully utilize temporal correlation of user activity and rely on known active user numbers. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme that uses 1-bit memory to piggyback information and a cross validation based adaptive subspace pursuit (CVASP) algorithm. Simulation results show that our proposed mechanism achieves similar performance while eliminating the need for prior knowledge.