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.

中文翻译：

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DeepMuD：通过深度学习对上行链路无赠款的NOMA IoT网络进行多用户检测

在这封信中，我们提出了一种在上行链路非正交多路访问（NOMA）中进行深度学习辅助的多用户检测（DeepMuD），以支持大规模的机器类型通信，其中离线训练的长短期内存（LSTM）-基于网络的网络用于多用户检测。在提出的DeepMuD中，由于它能够在导频/帧比非常低的情况下执行联合信道估计和具有导频响应的多用户检测，因此也不需要完美的信道状态信息（CSI）。提出的DeepMuD显着改善了上行NOMA的错误性能，并且优于传统的检测器（即使具有完美的CSI）。此外，随着物联网（IoT）设备数量的增加，这种收益变得无与伦比。此外，提议的DeepMuD具有灵活的检测功能，无论IoT设备数量如何，都可以执行多用户检测。因此，可以在没有信令开销的情况下为任意数量的IoT设备提供服务，从而实现了无授权通信。