Online Reconfigurable Deep Learning-Aided Multi-User Detection for IoT

Deep learning has been exploited to tackle the multi-user detection problem of non-orthogonal multiple access (NOMA), for its high detection accuracy and low computational delay. Existing deep learning algorithms adopt an offline training and online deploying method. When the online system configuration, such as the number of the users, differs from that in offline training, existing deep learning algorithms fail to work due to the mismatch of the output dimensions. In this paper, we propose an online reconfigurable deep learning framework for multi-user detection which can adapt to diversified number of the users. Inspired by the factor graph representation of NOMA, the framework is designed as the composition of several interlinked deep neural network branches where each branch is dedicated for the detection of a single user. The connections among the branches are configurable to achieve online dynamic extension or clipping so as to match the varying number of NOMA users. Experiments validate the online reconfigurability and the performance gain of the proposed deep learning framework.