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As a means to support the access of massive machine-type communicationdevices, grant-free access and non-orthogonal multiple access (NOMA) havereceived great deal of attention in recent years. In the grant-freetransmission, each device transmits information without the granting process sothat the basestation needs to identify the active devices among all potentialdevices. This process, called an active user detection (AUD), is a challengingproblem in the NOMA-based systems since it is difficult to identify activedevices from the superimposed received signal. An aim of this paper is to putforth a new type of AUD based on deep neural network (DNN). By applying thetraining data in the properly designed DNN, the proposed AUD scheme learns thenonlinear mapping between the received NOMA signal and indices of activedevices. As a result, the trained DNN can handle the whole AUD process,achieving an accurate detection of the active users. Numerical resultsdemonstrate that the proposed AUD scheme outperforms the conventionalapproaches in both AUD success probability and computational complexity.

Deep Neural Network Based Active User Detection for Grant-free NOMA Systems