Dynamic User Activity and Data Detection for Grant-Free NOMA via Weighted ℓ2,1 Minimization

Abstract

Grant-free non-orthogonal multiple access (NOMA) has recently received wide attention for reducing signaling overhead and transmission latency in massive machine-type communications (mMTC). In grant-free NOMA systems, user activity and data (UAD) has to be detected, which is challenging in practice. As an emerging technique, compressive sensing (CS) shows great promise in solving this problem by exploiting the inherent sparsity nature of user activity. This paper proposes to use the weighted <inline-formula> <tex-math notation="LaTeX">$\ell _{2, 1}$ </tex-math></inline-formula> minimization (WL21M) to jointly detect UAD in realistic dynamic scenarios. At first, the average recoverability of the WL21M is analyzed. This analysis reveals the fact that the WL21M can improve the detection performance by means of an appropriate weighting and the incorporation of intrinsic temporal correlation. Motivated by the analysis, a collaborative hierarchical match pursuit (C-HiMP) algorithm is proposed for dynamic UAD detection. In the C-HiMP, a sequence of WL21M problems are solved in the subspaces spanned by all of the components in the hierarchical estimated support sets, where the weights are collaboratively updated by the solutions in previous time slots so that an attractive self-correction capacity is obtained. Simulation results demonstrate that the proposed C-HiMP can obtain significant performance improvements, in terms of detection accuracy and computational complexity, compared with several state-of-the-art CS-based detection algorithms.