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2019

Deep Learning-Aided Trainable Projected Gradient Decoding for LDPC Codes

Wadayama, Tadashi, Takabe, Satoshi

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We present a novel optimization-based decoding algorithm for LDPC codes that is suitable for hardware architectures specialized to feed-forward neural networks.

The algorithm is based on the projected gradient descent algorithm with a penalty function for solving a non-convex minimization problem.
The proposed algorithm has several internal parameters such as step size parameters, a softness parameter, and the penalty coefficients.
We use a standard tool set of deep learning, i.e., back propagation and stochastic gradient descent (SGD) type algorithms, to optimize these parameters.

Deep Learning-Aided Trainable Projected Gradient Decoding for LDPC Codes · Around

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Similar

K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” in Proc. 27th Int. Conf. Machine Learning, pp. 399–406, 2010
2010
Cited alongside, same era.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980, 2014
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Cited alongside, same era.
E. Nachmani, Y. Beéry, and D. Burshtein, “Learning to decode linear codes using deep learning,” 2016 54th Annual Allerton Conf. Comm., Control, and Computing
2016
Cited alongside, same era.
PyTorch, https://pytorch.org
Cited in the paper.

Then

X. Liu and S. C. Draper, “The ADMM penalized decoder for LDPC codes, ” IEEE Transactions on Information Theory, Vol. 62 , Issue: 6, pp. 2966 - 2984, 2016
2016
Later among the works it cites.
D. Ito, S. Takabe, and T. Wadayama, “Trainable ISTA for sparse signal recovery,” IEEE Int. Conf. Comm., Workshop on Promises and Challenges of Machine Learning in Communication Networks, Kansas city, May. 2018. (arXiv:1801.01978)
Original
2018
Later among the works it cites.

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