Deep-Learning-Aided Successive Cancellation List Flip Decoding for Polar Codes

2026-05-21

AI Summary

This work addresses the limited decoding performance of polar codes under finite block lengths by proposing a novel approach that integrates deep learning with successive cancellation list flipping (SCLF) decoding. Specifically, a stacked LSTM network is employed to perform end-to-end training using key features such as log-likelihood ratios (LLRs), enabling joint prediction of multiple erroneous bit positions and optimal flipping strategies—including flip-1, flip-2, and continue-flipping decisions. The proposed method significantly enhances the accuracy of error-bit identification while reducing the average number of decoding attempts, thereby achieving superior error-correction performance compared to state-of-the-art algorithms.

Abstract

Polar codes are the first error-correcting code proven to achieve channel capacity based on infinite code length. The Successive Cancellation List Flip (SCLF) decoding algorithm was proposed by flipping an erroneous bit during the next decoding attempt. To identify the erroneous bits, the Log-Likelihood Ratio (LLR) is used to indicate the reliability of each decision bit. To improve the accuracy of the erroneous bit prediction, we propose deep-learning-aided (DL-aided) SCLF decoding algorithms. We first offer a stacked LSTM network that contains new features to train our models, which are able to improve the accuracy of the prediction of positions of erroneous bits. Then we separately train the stacked LSTM models to predict the position of both the first and second erroneous bits and whether to continue flipping. As a result, the DL-aided SCLF decoding algorithms based on the proposed stacked LSTM \mbox{flip-1} model, stacked LSTM \mbox{flip-2} model, and the stacked LSTM \mbox{continue-flipping} check (CFC) model are able to provide a better performance at a lower number of average decoding attempts when compared to other state-of-the-art decoding algorithms.

Problem

• Polar Codes
• Successive Cancellation List Flip Decoding
• Erroneous Bit Prediction
• Deep Learning
• Log-Likelihood Ratio

Innovation

• deep learning
• polar codes
• SCLF decoding
• stacked LSTM
• error bit prediction