  As a means to support the access of massive machine-type communication
devices, grant-free access and non-orthogonal multiple access (NOMA) have
received great deal of attention in recent years. In the grant-free
transmission, each device transmits information without the granting process so
that the basestation needs to identify the active devices among all potential
devices. This process, called an active user detection (AUD), is a challenging
problem in the NOMA-based systems since it is difficult to identify active
devices from the superimposed received signal. An aim of this paper is to put
forth a new type of AUD based on deep neural network (DNN). By applying the
training data in the properly designed DNN, the proposed AUD scheme learns the
nonlinear mapping between the received NOMA signal and indices of active
devices. As a result, the trained DNN can handle the whole AUD process,
achieving an accurate detection of the active users. Numerical results
demonstrate that the proposed AUD scheme outperforms the conventional
approaches in both AUD success probability and computational complexity.
