Learning to decode linear codes using deep learning
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Deep unfolding has been recently applied in channel decoding and MIMO detection.
“It has been recently applied in the context of channel decoding and MIMO detection [22], [23], [24].”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [22]
In [22], training with a single codeword is sufficient because the code structure is embedded in the NN through the Tanner graph.
“For instance in [22], it was shown that training with a single codeword is sufficient since the structure of the code is embedded in the NN through the Tanner graph.”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [22]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382 · see citing works
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322 · see citing works
Flagged first, then by citations
Research works printed but not linked: 15
- A. Graves, A.-r. Mohamed, and G. Hinton, "Speech recognition with deep recurrent neural networks," in 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013, pp. 6645-6649.
- D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., "Mastering the game of go with deep neural networks and tree search," Nature, vol. 529, no. 7587, pp. 484-489, 2016.
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009, pp. 248-255.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.
- N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
- R. G. Gallager, Low Density Parity Check Codes. Cambridge, Massachusetts: M.I.T. Press, 1963.
- J. Jiang and K. R. Narayanan, "Iterative soft-input soft-output decoding of reed-solomon codes by adapting the parity-check matrix," IEEE Transactions on Information Theory, vol. 52, no. 8, pp. 3746-3756, 2006.
- I. Dimnik and Y. Be'ery, "Improved random redundant iterative hdpc decoding," IEEE Transactions on Communications, vol. 57, no. 7, pp. 1982-1985, 2009.
- A. Yufit, A. Lifshitz, and Y. Be'ery, "Efficient linear programming decoding of hdpc codes," IEEE Transactions on Communications, vol. 59, no. 3, pp. 758-766, 2011.
- X. Zhang and P. H. Siegel, "Adaptive cut generation algorithm for improved linear programming decoding of binary linear codes," IEEE Transactions on Information Theory, vol. 58, no. 10, pp. 6581-6594, 2012.
- M. Helmling, E. Rosnes, S. Ruzika, and S. Scholl, "Efficient maximum-likelihood decoding of linear block codes on binary mem- oryless channels," in 2014 IEEE International Symposium on Infor- mation Theory. IEEE, 2014, pp. 2589-2593.
- T. Richardson and R. Urbanke, Modern Coding Theory. Cambridge, UK: Cambridge University Press, 2008.
- M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al., "Tensorflow: Large-scale machine learning on heterogeneous systems, 2015," Software available from tensorflow. org, vol. 1, 2015.
- T. Tieleman and G. Hinton, "Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude," COURSERA: Neural Networks for Machine Learning, vol. 4, no. 2, 2012.
- G. Hinton, "A practical guide to training restricted boltzmann ma- chines," Momentum, vol. 9, no. 1, p. 926, 2010.
Cited by
Sorted by citations
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980 · see citing works
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Ye, H., Li, G. Y., & Juang, B.-H. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2017.2757490 · Cited by 2,000 · see citing works
Deep Learning Based Communication Over the Air
Dorner, S., Cammerer, S., Hoydis, J., & Brink, S. T. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2784180 · Cited by 859 · see citing works
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Xia, W., Zheng, G., Zhu, Y., Zhang, J., Wang, J., & Petropulu, A. P. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2960361 · Cited by 345 · see citing works
“A lot of research has attempted to use DL to address physical layer issues, including channel decoding [17, 18], detection [19–21], channel estimation [22–24], and resource management [25–32].”
BackgroundChannel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN
Ye, H., Li, G. Y., Juang, B.-H. F., & Sivanesan, K. (2018) · 2018 IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644250 · Cited by 244 · see citing works
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Nguyen, D. C., Cheng, P., Ding, M., Lopez-Perez, D., Pathirana, P. N., Li, J., Seneviratne, A., Li, Y., & Poor, H. V. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3024783 · Cited by 160 · see citing works
LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks
Jiang, Y., Kim, H., Asnani, H., Kannan, S., Oh, S., & Viswanath, P. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2988577 · Cited by 87
Model Refinement Learning and an Example on Channel Estimation With Universal Noise Model
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- Authors
- Nachmani, E., Be'ery, Y., & Burshtein, D.
- Venue
- 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
- Publisher
- IEEE
- Published
- 2016-09
- Pages
- 341-346
- Cited by
- 460
- References
- 4
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