  Grant-free non-orthogonal multiple access (NOMA) is considered as one of the
supporting technology for massive connectivity for future networks. In the
grant-free NOMA systems with a massive number of users, user activity detection
is of great importance. Existing multi-user detection (MUD) techniques rely on
complicated update steps which may cause latency in signal detection. In this
paper, we propose a generative neural network-based MUD (GenMUD) framework to
utilize low-complexity neural networks, which are trained to reconstruct
signals in a small fixed number of steps. By exploiting the uncorrelated user
behaviours, we design a network architecture to achieve higher recovery
accuracy with a low computational cost. Experimental results show significant
performance gains in detection accuracy compared to conventional solutions
under different channel conditions and user sparsity levels. We also provide a
sparsity estimator through extensive experiments. Simulation results of the
sparsity estimator showed high estimation accuracy, strong robustness to
channel variations and neglectable impact on support detection accuracy.
