An Introduction to Neural Networks in SCMA

Abstract

Sparse Code Multiple Access (SCMA) has proved to be a fascinating research in order to curtail the complications faced by the wireless communication networks. SCMA being a Non-Orthogonal Multiple Access technique evinces to be an outstanding candidate, to cater the complications faced by 5G communication networks to improve the bit error rate and reduce the complexity of decoding the transmitted signal from received signal. This paper explains the concept of SCMA by explaining the basic structure of encoder, decoder and codebook design with the help of neural networks. It explains the concept of reducing the complexity of the traditional decoder of the SCMA by implementing Neural Networks. Further sections explain the use of Convolutional Neural Networks for blind decoding, that outperforms the complexity of decoding carried by conventional SCMA using Message Passing Algorithm. This further explains the use of Deep Neural Networks for designing the codebook and decoding it, by adopting an autoencoder structure.