In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural network. The neural network can be trained off-line and then deployed for online detection. By further refining the network weights corresponding to the edges of a factor graph, the proposed method achieves a better performance. Moreover, the deep neural network based detection is a computationally efficient since highly paralleled computations in the network are enabled in emerging Artificial Intelligence (AI) chips.
@article{arxiv.1808.08015,
title = {An Enhanced SCMA Detector Enabled by Deep Neural Network},
author = {Chao Lu and Wei Xu and Hong Shen and Hua Zhang and Xiaohu You},
journal= {arXiv preprint arXiv:1808.08015},
year = {2018}
}