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

IEEE International Conference on Computer and Communications (ICCC)

Low Complexity Deep Learning Method for Base Graph of Protograph LDPC Decoding

Shuo Peng Liang, Xingcheng Liu, Lin Zhang

Published Dec 8, 2023• 0 citations
Publisher

Abstract

Deep learning-based decoding algorithms have emerged as a focal point currently in contemporary research. Neural Offset Min-Sum Belief Propagation decoding algorithm is one of the traditional deep learning methods rooted in model-driven approaches. However, with the gradual increase in codeword length, there is an inevitable surge in the training cost and time complexity of such kind of deep learning algorithms. Addressing this issue, a novel deep learning algorithm based on the structural characteristics of the base graph in protograph LDPC codes is proposed. By strategically training the base graph and introducing a unique bit decoding allocation strategy, we endeavor to harmonize the Deep Learning algorithm with the intrinsic structure of the base graph itself. This approach contrasts favorably with several existing methods that utilize conventional segmental deep learning decoding algorithms for protograph LDPC codes. Simulation results demonstrate that, compared to other model-driven based deep learning algorithms, the proposed algorithm significantly reduces the number of parameters required for storage and training, while the loss in decoding performance remains within acceptable bounds.

DOI
10.1109/iccc59590.2023.10507610

Related papers

Learning to Decode Protograph LDPC Codes

Jincheng Dai, Kailin Tan, Zhongwei Si +4 more

The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works is that the scale of neural networks increases rapidly with the codelength, thus they can only support short to moderate codelengths. From the point view of practicality, we propose a high-performance neural min-sum (MS) decoding method that makes full use of the lifting structure of protograph low-density parity-check (LDPC) codes. By this means, the size of the parameter array of each layer in the neural decoder only equals the number of edge-types for arbitrary codelengths. In particular, for protograph LDPC codes, the proposed neural MS decoder is constructed in a special way such that identical parameters are shared by a bundle of edges derived from the same edge-type. To reduce the complexity and overcome the vanishing gradient problem in training the proposed neural MS decoder, an iteration-by-iteration (i.e., layer-by-layer in neural networks) greedy training method is proposed. With this, the proposed neural MS decoder tends to be optimized with faster convergence, which is aligned with the early termination mechanism widely used in practice. To further enhance the generalization ability of the proposed neural MS decoder, a codelength/rate compatible training method is proposed, which randomly selects samples from a set of codes lifted from the same base code. As a theoretical performance evaluation tool, a trajectory-based extrinsic information transfer (T-EXIT) chart is developed for various decoders. Both T-EXIT and simulation results show that the optimized MS decoding can provide faster convergence and up to 1dB gain compared with the plain MS decoding and its variants with only slightly increased complexity. In addition, it can even outperform the sum-product algorithm for some short codes.

arXiv:2102.03828•IEEE Journal on Selected Areas in Communications•May 10, 2021•80 citations

A Low Complexity Model-Driven Deep Learning LDPC Decoding Algorithm

Qingle Wu, Su-Kit Tang, Yuanhui Liang +2 more

A novel Neural Offset Min-Sum(NOMS) Belief Propagation(BP) decoding algorithm based on model-driven is proposed which applied to LDPC decoding. NOMS is improved multiplication in Neural Normalized Min-Sum(NNMS) into addition operation to reduce the complexity of calculation., a better Bit Error Rate (BER) performance is simultaneously achieved in the same condition. Secondly, considering that there are still many multiplication operations in NOMS, we propose a novel Shared Offset Min-Sum(SNOMS) to reduce the number of weights in the network by sharing parameters. Finally, codebook-based quantization is used to further reduce the memory consumption. Simulation experimental results show that the proposed method has a better BER performance, and the decoding accuracy of the decoder is 0.65dB higher than that of the NNMS after 5 iterations. In addition, SNOMS decoding method achieves almost the same decoding performance comparable to that of NOMS, but requires less complex calculation. Proposed quantization of code-book method reduces memory requirement significantly with slight performance loss.

International Conference on Computer and Communication Systems (ICCCS)•Apr 23, 2021•6 citations

Customized Branched Neural Network-Aided Shuffled Min-Sum Decoder for Protograph LDPC Codes

Yurong Wang, Liang Lv, Yi Fang +2 more

The paper designs a novel neural shuffled min-sum (NSMS) decoder with the model-driven deep learning method to achieve higher efficient and lower complexity decoding for protograph low-density parity-check (LDPC) codes. We propose a new type of customized branched neural network (CBNN) structure, which integrates shuffled min-sum (SMS) decoding algorithm and shuffled belief-propagation (SBP) decoding algorithm. In such a network structure, we can adjust layer arrangement and simplify parameter groups at a specific stage (i.e., training or inference stage) to reduce the unwarranted computational workload. Furthermore, we utilize the branched neuron mean difference (BNMD) to optimize the training targets of the proposed NSMS decoder, which significantly accelerates the convergence speed of the network. Analytical and simulation results show that the proposed NSMS decoder can achieve better performance than the state-of-the-art counterparts in terms of convergence speed, error rate and computational complexity.

IEEE Transactions on Vehicular Technology•Sep 12, 2024•1 citation

Low-Complexity Neural Belief Propagation Algorithm for LDPC Decoding

Haojia Zhang, Shuai Han, Hao Chen

With the rapid development of deep learning research and physical layer communication applications, deep learning-based channel coding has gradually become a research hotspot. In this paper, we propose a LDPC decoding network based on neural belief propagation (NBP) decoding. By introducing weights sharing mechanism, the weights vary across different iterations. The number of parameters is reduced, which significantly lowers memory requirements. In addition, we optimize the loss function to better train the model, achieving a lower bit error rate (BER) performance. Experimental results show that the proposed decoder yields significant performance improvements with respect to NBP, and significantly reducing the number of learnable weights.

International Wireless Communications and Mobile Computing Conference (IWCMC)•May 12, 2025•4 citations

A Layer-Wise Syndrome-Based Framework for LDPC Decoding with Deep Learning

Ziwen Ren

This paper proposes a newly developed layer-wise syndrome-based framework for decoding Low-Density Parity-Check (LDPC) codes using deep learning. The framework leverages a layer-specific training approach to design methodologically effective decoding strategies, aiming to utilize the specific characteristics of each layer and improve overall decoding performance. By integrating the Normalized Offset Min-Sum (NOMS) algorithm into a Forward-Feedback Neural Network (NOMS-FF), the proposed model employs a soft-syndrome loss function to assist the learning of codeword structures and optimize decoding performance across varying noise levels. Additionally, the framework incorporates a layer-freezing strategy and dynamic Signal-to-Noise Ratio (SNR) allocation, enabling targeted post-training adjustments tailored to layer-specific characteristics. Experimental results on 6G-candidate QC-LDPC codes (Base Graph 2, N=240,K=80 ) demonstrate that the proposed approach provides substantial performance advantages over traditional decoders across the entire SNR range tested in this study. Furthermore, this study conducts partial-layer decoding experiments, which demonstrate that using only a subset of neural network layers can reduce the model’s computational runtime while simultaneously maintaining or improving decoding accuracy compared to the full-layer model under specific noise conditions.

•Jan 1, 2026•0 citations

Normalized Neural Network for Belief Propagation LDPC Decoding

Yiduo Tang, Lin Zhou, Shuying Zhang +2 more

BP decoding algorithm is one of the commonly used decoding algorithms for LDPC codes. To adapt LDPC codes to different 5G scenarios and further improve the decoding performance of short LDPC codes, a scheme combining model-driven deep learning with a traditional BP decoding algorithm is proposed. With the advantages of model-driven, this solution expands the decoding iteration process between the check node and the variable node into a neural network and proposes a parameter normalization optimization solution to solve the problem of the program with many training parameters, the edge weights of the optimized Tanner graph are re-assigned and bound. Simulation results show that the proposed scheme can improve the decoding performance of LDPC codes with short lengths.

IEEE International Conference on Networking, Sensing and Control (ICNSC)•Dec 3, 2021•8 citations
Deep learning-based decoding algorithms have emerged as a focal point currently in contemporary research. Neural Offset Min-Sum Belief Propagation decoding algorithm is one of the traditional deep learning methods rooted in model-driven approaches. However, with the gradual increase in codeword length, there is an inevitable surge in the training cost and time complexity of such kind of deep learning algorithms. Addressing this issue, a novel deep learning algorithm based on the structural characteristics of the base graph in protograph LDPC codes is proposed. By strategically training the base graph and introducing a unique bit decoding allocation strategy, we endeavor to harmonize the Deep Learning algorithm with the intrinsic structure of the base graph itself. This approach contrasts favorably with several existing methods that utilize conventional segmental deep learning decoding algorithms for protograph LDPC codes. Simulation results demonstrate that, compared to other model-driven based deep learning algorithms, the proposed algorithm significantly reduces the number of parameters required for storage and training, while the loss in decoding performance remains within acceptable bounds.
The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works is that the scale of neural networks increases rapidly with the codelength, thus they can only support short to moderate codelengths. From the point view of practicality, we propose a high-performance neural min-sum (MS) decoding method that makes full use of the lifting structure of protograph low-density parity-check (LDPC) codes. By this means, the size of the parameter array of each layer in the neural decoder only equals the number of edge-types for arbitrary codelengths. In particular, for protograph LDPC codes, the proposed neural MS decoder is constructed in a special way such that identical parameters are shared by a bundle of edges derived from the same edge-type. To reduce the complexity and overcome the vanishing gradient problem in training the proposed neural MS decoder, an iteration-by-iteration (i.e., layer-by-layer in neural networks) greedy training method is proposed. With this, the proposed neural MS decoder tends to be optimized with faster convergence, which is aligned with the early termination mechanism widely used in practice. To further enhance the generalization ability of the proposed neural MS decoder, a codelength/rate compatible training method is proposed, which randomly selects samples from a set of codes lifted from the same base code. As a theoretical performance evaluation tool, a trajectory-based extrinsic information transfer (T-EXIT) chart is developed for various decoders. Both T-EXIT and simulation results show that the optimized MS decoding can provide faster convergence and up to 1dB gain compared with the plain MS decoding and its variants with only slightly increased complexity. In addition, it can even outperform the sum-product algorithm for some short codes.
A novel Neural Offset Min-Sum(NOMS) Belief Propagation(BP) decoding algorithm based on model-driven is proposed which applied to LDPC decoding. NOMS is improved multiplication in Neural Normalized Min-Sum(NNMS) into addition operation to reduce the complexity of calculation., a better Bit Error Rate (BER) performance is simultaneously achieved in the same condition. Secondly, considering that there are still many multiplication operations in NOMS, we propose a novel Shared Offset Min-Sum(SNOMS) to reduce the number of weights in the network by sharing parameters. Finally, codebook-based quantization is used to further reduce the memory consumption. Simulation experimental results show that the proposed method has a better BER performance, and the decoding accuracy of the decoder is 0.65dB higher than that of the NNMS after 5 iterations. In addition, SNOMS decoding method achieves almost the same decoding performance comparable to that of NOMS, but requires less complex calculation. Proposed quantization of code-book method reduces memory requirement significantly with slight performance loss.
The paper designs a novel neural shuffled min-sum (NSMS) decoder with the model-driven deep learning method to achieve higher efficient and lower complexity decoding for protograph low-density parity-check (LDPC) codes. We propose a new type of customized branched neural network (CBNN) structure, which integrates shuffled min-sum (SMS) decoding algorithm and shuffled belief-propagation (SBP) decoding algorithm. In such a network structure, we can adjust layer arrangement and simplify parameter groups at a specific stage (i.e., training or inference stage) to reduce the unwarranted computational workload. Furthermore, we utilize the branched neuron mean difference (BNMD) to optimize the training targets of the proposed NSMS decoder, which significantly accelerates the convergence speed of the network. Analytical and simulation results show that the proposed NSMS decoder can achieve better performance than the state-of-the-art counterparts in terms of convergence speed, error rate and computational complexity.
With the rapid development of deep learning research and physical layer communication applications, deep learning-based channel coding has gradually become a research hotspot. In this paper, we propose a LDPC decoding network based on neural belief propagation (NBP) decoding. By introducing weights sharing mechanism, the weights vary across different iterations. The number of parameters is reduced, which significantly lowers memory requirements. In addition, we optimize the loss function to better train the model, achieving a lower bit error rate (BER) performance. Experimental results show that the proposed decoder yields significant performance improvements with respect to NBP, and significantly reducing the number of learnable weights.
This paper proposes a newly developed layer-wise syndrome-based framework for decoding Low-Density Parity-Check (LDPC) codes using deep learning. The framework leverages a layer-specific training approach to design methodologically effective decoding strategies, aiming to utilize the specific characteristics of each layer and improve overall decoding performance. By integrating the Normalized Offset Min-Sum (NOMS) algorithm into a Forward-Feedback Neural Network (NOMS-FF), the proposed model employs a soft-syndrome loss function to assist the learning of codeword structures and optimize decoding performance across varying noise levels. Additionally, the framework incorporates a layer-freezing strategy and dynamic Signal-to-Noise Ratio (SNR) allocation, enabling targeted post-training adjustments tailored to layer-specific characteristics. Experimental results on 6G-candidate QC-LDPC codes (Base Graph 2, N=240,K=80 ) demonstrate that the proposed approach provides substantial performance advantages over traditional decoders across the entire SNR range tested in this study. Furthermore, this study conducts partial-layer decoding experiments, which demonstrate that using only a subset of neural network layers can reduce the model’s computational runtime while simultaneously maintaining or improving decoding accuracy compared to the full-layer model under specific noise conditions.
BP decoding algorithm is one of the commonly used decoding algorithms for LDPC codes. To adapt LDPC codes to different 5G scenarios and further improve the decoding performance of short LDPC codes, a scheme combining model-driven deep learning with a traditional BP decoding algorithm is proposed. With the advantages of model-driven, this solution expands the decoding iteration process between the check node and the variable node into a neural network and proposes a parameter normalization optimization solution to solve the problem of the program with many training parameters, the edge weights of the optimized Tanner graph are re-assigned and bound. Simulation results show that the proposed scheme can improve the decoding performance of LDPC codes with short lengths.