Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access [article]

Thushan Sivalingam, Samad Ali, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-Aho
2021 arXiv   pre-print
Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden
more » ... odes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.
arXiv:2106.11204v1 fatcat:hod2frjuxrc37d4zahynsmwyhi