Convolutional Neural Networks for blind decoding in Sparse Code Multiple Access

Abstract

Sparse code multiple access (SCMA) has attracted growing research interests in order to meet the targets of the next generation of wireless communication networks. Since it relies on non-orthogonal multiple access (NOMA) techniques, it is considered as a promising candidate for future systems that can improve the spectral efficiency and solve the problem of massive user connections. In this paper, the basic concept of SCMA is introduced, including SCMA encoding, codebook mapping, and SCMA decoding. The major challenge of SCMA is the very high detection complexity. Then, a novel strategy for blind decoding based on convolutional neural networks is proposed. Through simulations, we showed that our proposed scheme outperforms conventional schemes in terms of both BER and computational complexity, where 0.9 dB improvements can be achieved.