Joint User Activity Identification and Channel Estimation for Grant-Free NOMA: A Spatial–Temporal Structure-Enhanced Approach

Abstract

Exploiting the sparse nature of user activity, compressed sensing (CS) has been a powerful technique to realize efficient user detection in grant-free nonorthogonal multiple access (NOMA). However, most of the existing CS-based multiuser detection schemes merely independently incorporate the temporal correlation in frame-based transmission or spatial correlation induced by multiantenna reception, leading to unsatisfactory user detection performance. Driven by the observation in the CS theory that the signal recovery performance could be enhanced by an increased number of sparse vectors with a common support set, in this article, we propose a novel joint user activity identification and channel estimation (JUICE) framework by integrating the temporal correlation of active user sets with multiantenna reception, which could achieve superior user detection performance. Specifically, we first formulate the JUICE as a Kronecker CS (KCS) problem to model the CS measurement process, by fully extracting the spatial-temporal structure of user activity. Then, based on the mined spatial-temporal structure of user activity, an adaptive subspace pursuit algorithm is developed, i.e., spatial-temporal structure enhanced adaptive subspace pursuit (STS-ASP), which could realize efficient multiuser detection. A distinct advantage of the proposed algorithm is that it does not require any prior knowledge (e.g., the number of active users and the noise level), by adaptively acquiring the number of active users and employing the cross-validation technique to appropriately terminate the iterative procedures. Extensive experimental evaluation is conducted, and the results corroborate the superiority of the proposed framework compared with the existing CS-based multiuser detection methods.