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We consider the multi-user detection (MUD) problem in uplink grant-freenon-orthogonal multiple access (NOMA), where the access point has to identifythe total number and correct identity of the active Internet of Things (IoT)devices and decode their transmitted data. We assume that IoT devices usecomplex spreading sequences and transmit information in a random-access mannerfollowing the burst-sparsity model, where some IoT devices transmit their datain multiple adjacent time slots with a high probability, while others transmitonly once during a frame. Exploiting the temporal correlation, we propose anattention-based bidirectional long short-term memory (BiLSTM) network to solvethe MUD problem. The BiLSTM network creates a pattern of the device activationhistory using forward and reverse pass LSTMs, whereas the attention mechanismprovides essential context to the device activation points. By doing so, ahierarchical pathway is followed for detecting active devices in a grant-freescenario. Then, by utilising the complex spreading sequences, blind datadetection for the estimated active devices is performed. The proposed frameworkdoes not require prior knowledge of device sparsity levels and channels forperforming MUD. The results show that the proposed network achieves betterperformance compared to existing benchmark schemes.

Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network