Sparse Autoencoder for Sparse Code Multiple Access

Abstract

In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum. To address this problem, we resort to the usage of an efficient deep learning technique, autoencoders, that club the encoder and the decoder part and automatically learn the most optimum codeword that could give the least BER. In this paper, SCMA sparse autoencoder, which is a variant of the autoencoder, is proposed, that has better BER performance than a conventional autoencoder, without paying in terms of computational complexity.