The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep Learning-Aided SCMA
Abstract
Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · cited 1×
“The received signal can be decoded using maximum likelihood (ML) decoding or via a message passing algorithm (MPA) [4].”
Method
Cited by
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Simeone, O. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2881442 · Cited by 516
Deep Learning for an Effective Nonorthogonal Multiple Access Scheme
Gui, G., Huang, H., Song, Y., & Sari, H. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2848294 · Cited by 505
Non-Orthogonal Multiple Access: Common Myths and Critical Questions
Vaezi, M., Schober, R., Ding, Z., & Poor, H. V. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800598 · Cited by 339
Towards Optimal Power Control via Ensembling Deep Neural Networks
Liang, F., Shen, C., Yu, W., & Wu, F. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2957482 · Cited by 268
Metadata
- Authors
- Kim, M., Kim, N.-I., Lee, W., & Cho, D.-H.
- Venue
- IEEE Communications Letters
- Published
- 2018-04
- Volume
- 22
- Issue
- 4
- Pages
- 720-723
- ISSN
- 1089-7798 (ISSN-L)
- OpenAlex
- W2784180017
- Cited by
- 201
- References
- 3
- FWCI
- 11.57
- Type
- journal article
- Language
- en
- Open access
- closed
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