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Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access
[article]
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
arXiv:2106.11204v1
fatcat:hod2frjuxrc37d4zahynsmwyhi