Benefiting from performance advantages under short code lengths, polar codes are well-suited for certain scenarios, such as the future Internet of Things (IoT) applications that require high reliability and low power. Existing list flip decoders can efficiently further enhance the error-correction performance of polar codes with finite code lengths, particularly the dynamic successive cancellation list flip (D-SCLF) decoder with flexible high-order error-correction capability (FHECC). However, to the best of our knowledge, current list flip decoders cannot effectively balance complexity and error-correction efficiency. To address this, we propose a parity-check-aided D-SCLF (PC-DSCLF) decoder. This decoder, based on FHECC and the characteristics of the list flip decoding process, introduces a simplified flip metric and a hybrid check scheme, along with a decoding method that supports the check scheme, enabling it to retain FHECC while achieving low complexity. Simulation results show that the proposed PC-DSCLF decoder achieves up to a 51.1\% average complexity reduction compared to the D-SCLF algorithm with distributed CRC for $PC(512, 256+24)$ Benefiting from performance advantages under short code lengths, polar codes are well-suited for certain scenarios, such as the future Internet of Things (IoT) applications that require high reliability and low power. Existing list flip decoders can efficiently further enhance the error-correction performance of polar codes with finite code lengths, particularly the dynamic successive cancellation list flip (D-SCLF) decoder with flexible high-order error-correction capability (FHECC). However, to the best of our knowledge, current list flip decoders cannot effectively balance complexity and error-correction efficiency. To address this, we propose a parity-check-aided D-SCLF (PC-DSCLF) decoder. This decoder, based on FHECC and the characteristics of the list flip decoding process, introduces a simplified flip metric and a hybrid check scheme, along with a decoding method that supports the check scheme, enabling it to retain FHECC while achieving low complexity. Simulation results show that the proposed PC-DSCLF decoder achieves up to a 51.1\% average complexity reduction compared to the D-SCLF algorithm with distributed CRC for $PC(512, 256+24)$

arXiv:2303.12609Mar 22, 20230 citations

Simplified SCL decoding algorithm of polar codes based on critical sets

Jianguo Yuan, Fengguo Zhang, Jingjie He +1 more

Optoelectronics LettersJun 1, 20233 citations

Complete State Critical Set Based SCLF Decoding Algorithm for Polar Codes

Wenjie Dai, Rui Guo

The revised critical set (RCS) used by successive cancellation list flip (SCLF) decoding algorithm only retains the unreliable bits of SC state.So, the bits of information in the RCS are not comprehensive enough.Aiming at this problem, this paper proposed a kind of complete state critical set (CSCS) based SCLF decoding algorithm for polar codes.In order to obtain a more complete flipping set of states, the proposed CSCS is constructed according to different path splitting states after pruning.According to different states of CSCS, hard decision and path metric (PM) correction methods are adopted respectively, to cut off the wrong path and keep the correct path, so as to achieve bit flipping function.Experimental results show that compared with the traditional SCLF decoding algorithm, the performance of complete state SCLF (CS-SCLF) decoding algorithm is improved.At the same time, the average flipping number of flips is reduced without loss of decoding performance.The revised critical set (RCS) used by successive cancellation list flip (SCLF) decoding algorithm only retains the unreliable bits of SC state.So, the bits of information in the RCS are not comprehensive enough.Aiming at this problem, this paper proposed a kind of complete state critical set (CSCS) based SCLF decoding algorithm for polar codes.In order to obtain a more complete flipping set of states, the proposed CSCS is constructed according to different path splitting states after pruning.According to different states of CSCS, hard decision and path metric (PM) correction methods are adopted respectively, to cut off the wrong path and keep the correct path, so as to achieve bit flipping function.Experimental results show that compared with the traditional SCLF decoding algorithm, the performance of complete state SCLF (CS-SCLF) decoding algorithm is improved.At the same time, the average flipping number of flips is reduced without loss of decoding performance.

KSII Transactions on Internet and Information SystemsDec 31, 20240 citations

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

Fu-Siang Liang, Shan Lu, Yeong-Luh Ueng

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 flip-1 model, stacked LSTM flip-2 model, and the stacked LSTM 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.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 flip-1 model, stacked LSTM flip-2 model, and the stacked LSTM 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.

arXiv:2605.23124IEEE Transactions on Cognitive Communications and NetworkingOct 20, 20235 citations

PC(512, 256+24)