Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Method of Critical Set Construction for Successive Cancellation List Decoder of Polar Codes Based on Deep Learning of Neural Networks

Fedor I. Ivanov, Feodosii Kotov, Zaikov Alexey

Published Jan 1, 2022• 1 citation
Publisher
DOI
10.2139/ssrn.4111931

Related papers

Method of Critical Set construction for Successive Cancellation List Decoder of Polar Codes Based on Deep Learning of Neural Networks

Feodosii Kotov, Fedor I. Ivanov, Ilya Timokhin

The Successive Cancellation List (SCL) algorithm is a widely used decoding technique in communication systems. However, constructing the critical set for SCL decoding is a challenging task, as it requires a large number of computations and can lead to significant decoding delays. In this paper, a new approach to critical set construction for SCL decoding is proposed, which is based on deep learning of neural networks with special structure and activation functions.The proposed method is shown to significantly reduce the cardinality of the critical set required for achieving the target frame error rate, when used with an SCL decoder with a list size of 8. Simulation results demonstrate that the proposed method outperforms existing methods in terms of reducing decoding delay, while maintaining high decoding accuracy.The key innovation of the proposed approach lies in the use of deep learning techniques to learn the structure of the critical set, which enables the construction of a more efficient and compact set. This approach has the potential to significantly improve the performance of SCL decoding in practical communication systems, where decoding delay is a critical factor.

International Symposium on Problems of Redundancy in Information and Control Systems (REDUNDANCY)•Oct 24, 2023•1 citation

Survey on Decoding Schemes of the Polar Code Using the Deep Learning

Jida Yang, Yuan Liu

Lecture notes in electrical engineering•Jan 1, 2023•2 citations

Recent Successive Cancellation Decoding Methods for Polar Codes

So‐Yeon Choi, Youngjoo Lee, Hoyoung Yoo

•Jan 1, 2020•0 citations

Deep Learning-Assisted Adaptive Dynamic-SCLF Decoding of Polar Codes

Jun Li, Liangyu Zhou, Zhengquan Li +4 more

Recently, the dynamic-successive cancellation list flip (D-SCLF) decoder has been proposed to improve the high-order flipping performance of existing successive cancellation list flip (SCLF) decoders in polar codes decoding. However, the D-SCLF decoder involves a large number of exponential and logarithmic operations, resulting in an exponential increase in computational complexity. To further improve the performance and reduce the average complexity of D-SCLF decoding, the deep learning-assisted adaptive dynamic-SCLF (DL-AD-SCLF) decoding is proposed in this paper. The error metric of D-SCLF decoding is re-derived, and an approximation scheme is proposed to reduce computational complexity. To compensate the loss of performance due to approximation, two learnable parameters are introduced. Customized neural network structures are proposed to optimize these learnable parameters according to the improved error metric by employing deep learning (DL), and the deep learning-assisted dynamic-SCLF (DL-D-SCLF) decoding is proposed. Furthermore, the adaptive list is introduced into the DL-D-SCLF decoding to further reduce decoding complexity. Simulation results show that the proposed decoder performance is improved up to 0.35dB and 0.25dB, the average complexity is reduced by up to 57.65% and 51.48% for single-bit and multi-bit flipping, respectively. Additionally, the proposed decoder exhibits good robustness to changes in code rates, code lengths, and channel conditions.

IEEE Transactions on Cognitive Communications and Networking•Jan 3, 2024•9 citations

Neural Successive Cancellation List Decoding of Polar Codes

Stefano Negrini, Velio Tralli

Neural Network Decoders (NNDs) have been recently considered and investigated as an alternative to the classical algorithms for decoding Polar Codes. In particular, a class of partitioned decoders has been proposed where modified Successive Cancellation (SC) or Belief Propagation (BP) algorithms exploit the neural network decoding of short sublocks. Although NNDs work as one-shot decoders with small decoding latency, the performance of these decoders, in comparison with classic approaches, still suffers from some losses; this fact limits their application to codes with short/medium length. In this article we introduce the novel Neural Successive Cancellation List (NSCL) decoder, as a partitioned version of the SC decoder operating on a list of L multiple decoding candidates that are built with the decoded sub-blocks obtained from a set of NNDs working in parallel at each partition. This decoder significantly improves the BER performance of the non-list version under the same code length. Hence, it reduces the intrinsic loss of performance given by the use of NNDs and allows the application of NN-based decoding to longer codewords.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)•Aug 1, 2020•0 citations

Low-Complexity List Successive-Cancellation Decoding of Polar Codes Using List Pruning

Chen Ji, YouZhe Fan, ChenYang Xia +4 more

IEEE Conference Proceedings•Jan 1, 2016•0 citations
The Successive Cancellation List (SCL) algorithm is a widely used decoding technique in communication systems. However, constructing the critical set for SCL decoding is a challenging task, as it requires a large number of computations and can lead to significant decoding delays. In this paper, a new approach to critical set construction for SCL decoding is proposed, which is based on deep learning of neural networks with special structure and activation functions.The proposed method is shown to significantly reduce the cardinality of the critical set required for achieving the target frame error rate, when used with an SCL decoder with a list size of 8. Simulation results demonstrate that the proposed method outperforms existing methods in terms of reducing decoding delay, while maintaining high decoding accuracy.The key innovation of the proposed approach lies in the use of deep learning techniques to learn the structure of the critical set, which enables the construction of a more efficient and compact set. This approach has the potential to significantly improve the performance of SCL decoding in practical communication systems, where decoding delay is a critical factor.
Recently, the dynamic-successive cancellation list flip (D-SCLF) decoder has been proposed to improve the high-order flipping performance of existing successive cancellation list flip (SCLF) decoders in polar codes decoding. However, the D-SCLF decoder involves a large number of exponential and logarithmic operations, resulting in an exponential increase in computational complexity. To further improve the performance and reduce the average complexity of D-SCLF decoding, the deep learning-assisted adaptive dynamic-SCLF (DL-AD-SCLF) decoding is proposed in this paper. The error metric of D-SCLF decoding is re-derived, and an approximation scheme is proposed to reduce computational complexity. To compensate the loss of performance due to approximation, two learnable parameters are introduced. Customized neural network structures are proposed to optimize these learnable parameters according to the improved error metric by employing deep learning (DL), and the deep learning-assisted dynamic-SCLF (DL-D-SCLF) decoding is proposed. Furthermore, the adaptive list is introduced into the DL-D-SCLF decoding to further reduce decoding complexity. Simulation results show that the proposed decoder performance is improved up to 0.35dB and 0.25dB, the average complexity is reduced by up to 57.65% and 51.48% for single-bit and multi-bit flipping, respectively. Additionally, the proposed decoder exhibits good robustness to changes in code rates, code lengths, and channel conditions.
Neural Network Decoders (NNDs) have been recently considered and investigated as an alternative to the classical algorithms for decoding Polar Codes. In particular, a class of partitioned decoders has been proposed where modified Successive Cancellation (SC) or Belief Propagation (BP) algorithms exploit the neural network decoding of short sublocks. Although NNDs work as one-shot decoders with small decoding latency, the performance of these decoders, in comparison with classic approaches, still suffers from some losses; this fact limits their application to codes with short/medium length. In this article we introduce the novel Neural Successive Cancellation List (NSCL) decoder, as a partitioned version of the SC decoder operating on a list of L multiple decoding candidates that are built with the decoded sub-blocks obtained from a set of NNDs working in parallel at each partition. This decoder significantly improves the BER performance of the non-list version under the same code length. Hence, it reduces the intrinsic loss of performance given by the use of NNDs and allows the application of NN-based decoding to longer codewords.