Efficient Multiuser Detection for Uplink Grant-Free NOMA via Weighted Block Coordinate Descend

Grant-free non-orthogonal multiple access (GFNOMA) technique is considered as a promising solution to address the bottleneck of ubiquitous connectivity in massive machine type communication (mMTC) scenarios. One of the challenging problems in uplink GF-NOMA systems is how to efficiently perform user activity detection and data detection. In this paper, a novel complexity-reduction weighted block coordinate descend (CR-WBCD) algorithm is proposed to address this problem. To be specific, we formulate the multi-user detection (MUD) problem in uplink GF-NOMA systems as a weighted l₂ minimization problem. Based on the block coordinate descend (BCD) framework, a closed-form solution involving dynamic user-specific weights is derived to adaptively identify the active users with high accuracy. Furthermore, a complexity reduction mechanism is developed for substantial computational cost saving. Simulation results demonstrate that the proposed algorithm enjoys bound-approaching detection performance with more than three-order of magnitude computational complexity reduction.