DeepMuD: Multi-user Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning
This addresses the problem of enabling efficient massive machine-type communication for IoT networks by allowing grant-free communication with flexible detection and reduced signaling overhead.
The paper tackles multi-user detection in uplink grant-free NOMA IoT networks by proposing DeepMuD, a deep learning-based method using an LSTM network for joint channel estimation and detection without perfect CSI, which significantly improves error performance and outperforms conventional detectors, especially as the number of IoT devices increases.
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.