Deep-Learning-Aided Successive-Cancellation Decoding of Polar Codes
Abstract
A deep-learning-aided successive-cancellation list (DL-SCL) decoding algorithm for polar codes is introduced with deep-learning-aided successive-cancellation (DL-SC) decoding being a specific case of it. The DL-SCL decoder works by allowing additional rounds of SCL decoding when the first SCL decoding attempt fails, using a novel bit-flipping metric. The proposed bit-flipping metric exploits the inherent relations between the information bits in polar codes that are represented by a correlation matrix. The correlation matrix is then optimized using emerging deep-learning techniques. Performance results on a polar code of length 128 with 64 information bits concatenated with a 24-bit cyclic redundancy check show that the proposed bit-flipping metric in the proposed DL-SCL decoder requires up to 66% fewer multiplications and up to 36% fewer additions, without any need to perform transcendental functions, and by providing almost the same error-correction performance in comparison with the state of the art.
Cite
@article{arxiv.1912.01086,
title = {Deep-Learning-Aided Successive-Cancellation Decoding of Polar Codes},
author = {Seyyed Ali Hashemi and Nghia Doan and Thibaud Tonnellier and Warren J. Gross},
journal= {arXiv preprint arXiv:1912.01086},
year = {2019}
}
Comments
2019 Asilomar Conference on Signals, Systems, and Computers