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A novel deep neural network based approach for sparse code multiple access
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DOI：10.1016/j.neucom.2019.11.066.png)
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Abstract
En 中文
Sparse code multiple access (SCMA) has been one of the non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G communication networks. Conventional SCMA approaches are confronting challenges in designing low-complexity high-accuracy decoding algorithm and constructing optimum codebooks. Fortunately, the recent spotlighted deep learning technologies are of significant potentials in solving many communication engineering problems. Inspired by this, we propose and train a deep neural network (DNN) called DL-SCMA to learn to decode SCMA modulated signals corrupted by additive white Gaussian noise (AWGN). An autoencoder called AE-SCMA is established and trained to generate optimal SCMA codewords and reconstruct original bits. Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed. Simulations show that the DNN SCMA decoder significantly outperforms the conventional message passing algorithm (MPA) in terms of bit error rate (BER), symbol error rate (SER) and computational complexity, and AE-SCMA also demonstrates better performances via constructing better SCMA codebooks. The performance of deep learning aided DCMA is superior to the SCMA. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Sparse code multiple access
Non-orthogonal multiple access
Machine learning
Dense code multiple access
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