Notice of Violation of IEEE Publication Principles: Channel Decoding Based on Complex-valued Convolutional Neural Networks

Abstract

Notice of Violation of IEEE Publication Principles<br>BR> “Channel Decoding Based on Complex-valued Convolutional Neural Networks” <br>by Lun Li, Guanghui Yu, Jin Xu, and Liguang Li <br> in the Proceedings of the 2nd 6G Wireless Summit (6G SUMMIT), March 2020 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This article contains significant portions of text from the article cited below that were paraphrased without attribution. <br><br> “Exploiting Noise Correlation for Channel Decoding with Convolutional Neural Networks” <br> by Fei Liang, Cong Shen, and Feng Wu <br> in the Proceedings of the IEEE International Conference on Communications (ICC), May 2018 <br><br> <br/> Inspired by the recent outcomes in deep learning, we propose a novel decoding architecture which concatenates a complex-valued convolutional neural network (CCNN) with a belief propagation (BP) decoder for combating correlated noise in the channel. The CCNN can exploit the complex noise correlation and yield a more accurate estimation of the channel noise. Depressing the influence of channel noise via the proposed architecture, the BP decoder can obtain better decoding performances. Furthermore, extensive experiments are carried out to analyze and verify performances of the proposed framework.