Convolutional Neural Networks for blind decoding in Sparse Code Multiple Access

Imen Abidi (1) , Moez Hizem (1) , Iness Ahriz (2, 3) , Maha Cherif (1) , Ridha Bouallegue (1)

1 Innov'com, Sup'com, University of Carthage Ariana, Tunis, Tunisia
2 CEDRIC - Centre d'études et de recherche en informatique et communications
3 CEDRIC - LAETITIA - CEDRIC. Traitement du signal et architectures électroniques

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.

Keywords: SCMA; deep learning; convolutional neural networks; MPA

Domains: Engineering Sciences [physics]; Machine Learning [cs.LG]; Signal and Image Processing; Electronics

Imen Abidi, Moez Hizem, Iness Ahriz, Maha Cherif, Ridha Bouallegue. Convolutional Neural Networks for blind decoding in Sparse Code Multiple Access. 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), Jun 2019, Tangier, Morocco. pp.2007-2012, ⟨10.1109/IWCMC.2019.8766707⟩. ⟨hal-04052319⟩