  In this letter, we propose a deep learning-aided multi-user detection
(DeepMuD) in uplink non-orthogonal multiple access (NOMA) to empower the
massive machine-type communication where an offline-trained Long Short-Term
Memory (LSTM)-based network is used for multi-user detection. In the proposed
DeepMuD, a perfect channel state information (CSI) is also not required since
it is able to perform a joint channel estimation and multi-user detection with
the pilot responses, where the pilot-to-frame ratio is very low. The proposed
DeepMuD improves the error performance of the uplink NOMA significantly and
outperforms the conventional detectors (even with perfect CSI). Moreover, this
gain becomes superb with the increase in the number of Internet of Things (IoT)
devices. Furthermore, the proposed DeepMuD has a flexible detection and
regardless of the number of IoT devices, the multi-user detection can be
performed. Thus, an arbitrary number of IoT devices can be served without a
signaling overhead, which enables the grant-free communication.
