Deep Learning Based Joint Detection and Decoding of Non-Orthogonal Multiple Access Systems

In recent years, many studies have applied deep learning techniques to the field of communication. Many of them have achieved reduced complexity or improved performance. In this paper, we apply deep learning techniques to joint multiuser detection and decoding of non-orthogonal multiple access system. We construct the neural network detector and decoder based on message passing algorithm (MPA) and belief propagation (BP) algorithm, respectively. And the decoder is cascaded with the detector to form a larger neural network. To achieve this, we assign weights to the edges of the respective factor graphs and then use deep learning approaches to train these weights to get better performance. Compared with the conventional method (i.e. MPA for multiuser detection and BP for decoding), the proposed method improves the joint detection and decoding performance, while the computational complexity is not greatly increased.