  Grant-free random access and uplink non-orthogonal multiple access (NOMA)
have been introduced to reduce transmission latency and signaling overhead in
massive machine-type communication (mMTC). In this paper, we propose two novel
group-based deep neural network active user detection (AUD) schemes for the
grant-free sparse code multiple access (SCMA) system in mMTC uplink framework.
The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional
codebook structure and the channel characteristic. This is accomplished through
the received signal which incorporates the sparse structure of device activity
with the training dataset. Moreover, the offline pre-trained model is able to
detect the active devices without any channel state information and prior
knowledge of the device sparsity level. Simulation results show that with
several active devices, the proposed schemes obtain more than twice the
probability of detection compared to the conventional AUD schemes over the
signal to noise ratio range of interest.
