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

Dianzi xuebao

Some Quantization Issues for Decoding of Low-Density Parity Check Codes with BP Algorithm

Sun Shao

Published Jan 1, 2003• 0 citations
OpenAlex

Abstract

The quantization scheme for the iterative belief propagation (BP) algorithm of low density parity check (LDPC) codes is studied.The effects of the bound,resolution,and other parameters for uniform and nonuniform quantization schemes are discussed.Good performance of decoding under properly designed quantization can be obtained as shown by simulations.Furthermore,we found that the quantization of high order for the mid variables of BP decoding algorithm can improve the performance and reduce the error floor of LDPC codes at regions of high signal to noise ratio.

Related papers

Fast quantized belief propagation algorithm for iteratively decoding LDPC codes

Xinmei Wang

A fast quantized belief propagation algorithm is proposed for decoding low-density parity-check(LDPC) codes. With operations performed by addressing tables, the proposed algorithm takes as operands the indices of labels rather than the quantization levels. The intermediate calculations have been contained in operation tables with much higher precision. The required storage can be significantly reduced by applying the symmetry property while keeping low time complexity. Without the increase in time complexity, it is convenient to realize the decoding with uniform quantization or any nonuniform quantizations that is designed for improving the performance. The time-varying decoder also becomes possible. This algorithm makes it possible to apply LDPC codes to practical systems or to realize fast simulation systems. Some simulation results are also given. The 6?bit logarithmic quantization results in gains of about 0.2?dB relative to the uniform one, approaching to the continuous decoding at low signal-to-noise ratios (SNRs) and yielding a loss of about 0.2?dB at high SNRs. Proper quantization of high order can achieve the performance of continuous decoding, and it may be superior to continuous decoding at high SNRs.

Jounal of Xidian University•Jan 1, 2002•0 citations

The research on quantization issues of quasi-cycle low-density parity check codes based on BP algorithm

XU Zhi-jian

The quantization issues of the belief propagation(BP)algorithm for the quasi-cycle low-density parity code(QC-LDPC)of 802.16estandard are studied in this paper.We consider the quantization problem of input signal and mid-variables from the following three aspects:the variables range,bits and methods of the quantization and make a performance simulation and contrast..Then,a innovative and efficient scheme of quantization is proposed in this paper.The quasi-cycle low-density parity code(QC-LDPC)is used here.It has a better performance and it is beneficial for implementation of hardware.Assuming the input is equiprobable signal and the maximum number of iteration is 10,comparing to the standard(non-quantization)BP algorithm,the proposed scheme keeps the lower bit error rate(BER)by means of the MATLAB simulation meanwhile the hardware complexity is greatly reduced.

Journal of Zhejiang University of Technology•Jan 1, 2014•0 citations

Improving BER Performance of LDPC Codes based on Intermediate Decoding Results

Esa Alghonaim, Mohamed Adnan Landolsi, Aiman Helmi El-Maleh

The paper presents a novel approach to reduce the bit error rate (BER) in iterative belief propagation (BP) decoding of low density parity check (LDPC) codes. The behavior of the BP algorithm is first investigated as a function of number of decoder iterations, and it is shown that typical uncorrected error patterns can be classified into 3 categories: oscillating, nearly-constant, or random-like, with a predominance of oscillating patterns at high Signal-to-Noise (SNR) values. A proposed decoder modification is then introduced based on tracking the number of failed parity check equations in the intermediate decoding iterations, rather than relying on the final decoder output (after reaching the maximum number of iterations). Simulation results with a rate ¿ (1024,512) progressive edge-growth (PEG) LDPC code show that the proposed modification can decrease the BER by as much as 10-to-40%, particularly for high SNR values.

International Conference on Signal Processing and Communications (SPCOM)•Nov 1, 2007•5 citations

A high‐performance belief propagation decoding algorithm for codes with short cycles

Ali Karimi-lenji, Monireh Houshmand, Fahimeh Zarmehi

Summary The simplicity of decoding is one of the most important characteristics of the low density parity check (LDPC) codes. Belief propagation (BP) decoding algorithm is a well‐known decoding algorithm for LDPC codes. Most LDPC codes with long lengths have short cycles in their Tanner graphs, which reduce the performance of the BP algorithm. In this paper, we present 2 methods to improve the BP decoding algorithm for LDPC codes. In these methods, the calculation of the variable nodes is controlled by using “multiplicative correction factor” and “additive correction factor.” These factors are obtained for 2 separate channels, namely additive white Gaussian noise (AWGN) and binary symmetric channel (BSC), as 2 functions of code and channel parameters. Moreover, we use the BP‐based method in the calculation of the check nodes, which reduces the required resources. Simulation results show the proposed algorithm has better performance and lower decoding error as compared to BP and similar methods like normalized‐BP and offset‐BP algorithms.

International Journal of Communication Systems•Feb 28, 2017•2 citations

Design and Implementation of Low Bit Error Rate of LDPC Decoder

Ashlesha P. Kshirsagar, Sandeep Kakde, Manish Devendra Chawhan +1 more

Many classes of high-performance Low-density parity check codes are based on parity check matrices composed of permutation sub matrices. The emulation-simulation framework further allows the algorithm and implementation to be iteratively redefined to improve the error floor performance of message passing decoder. Log-Like hood-Ratio (LLR) based Belief-Propagation (BP) algorithm is presented for Low Density Parity Check codes. Numerically accurate representation of check node update computation used in LLR-BP decoding is described. The implementation of Sum-Product algorithm (SPA) within Low Density Parity Check Code (LDPC) decoder is described in this paper and the correction term is used to improve the decoding performance of min-sum algorithm (MSA). Quantization and log-tanh function approximation in sum-product decoder strongly affect which absorbing set dominates in error floor region. For LDPC decoder, bit error rate (BER) decreases with increase in the signal to noise ratio.

International Conference on Communication Systems and Network Technologies•Apr 1, 2015•1 citation

A Modified belief propagation decoding algorithm for low-density parity-check codes based on oscillating iteration

Bao Jian-rong

According to no-convergence of falsely oscillating iteration in the low-density parity-check(LDPC) decoding at the range from medium to high SNRs(Signal to Noise Ratios),we propose a modified LDPC belief propagation(BP) decoding algorithm,i.e.soft value zero-forcing BP algorithm.By setting extrinsic information of the oscillating iteration bit nodes into zero,the impact on the iteratively decoding from false channel information is greatly reduced.And it also improves the performance of the decoding algorithm.Furthermore,a decision criterion of oscillating iteration nodes is presented to increase the accuracy of the decision.Simulation results show that the proposed algorithm has better decoding performance than that of the BP algorithm with the same iterations at medium and high SNRs.

Journal of Circuits and Systems•Jan 1, 2012•0 citations
The quantization scheme for the iterative belief propagation (BP) algorithm of low density parity check (LDPC) codes is studied.The effects of the bound,resolution,and other parameters for uniform and nonuniform quantization schemes are discussed.Good performance of decoding under properly designed quantization can be obtained as shown by simulations.Furthermore,we found that the quantization of high order for the mid variables of BP decoding algorithm can improve the performance and reduce the error floor of LDPC codes at regions of high signal to noise ratio.
A fast quantized belief propagation algorithm is proposed for decoding low-density parity-check(LDPC) codes. With operations performed by addressing tables, the proposed algorithm takes as operands the indices of labels rather than the quantization levels. The intermediate calculations have been contained in operation tables with much higher precision. The required storage can be significantly reduced by applying the symmetry property while keeping low time complexity. Without the increase in time complexity, it is convenient to realize the decoding with uniform quantization or any nonuniform quantizations that is designed for improving the performance. The time-varying decoder also becomes possible. This algorithm makes it possible to apply LDPC codes to practical systems or to realize fast simulation systems. Some simulation results are also given. The 6?bit logarithmic quantization results in gains of about 0.2?dB relative to the uniform one, approaching to the continuous decoding at low signal-to-noise ratios (SNRs) and yielding a loss of about 0.2?dB at high SNRs. Proper quantization of high order can achieve the performance of continuous decoding, and it may be superior to continuous decoding at high SNRs.
The quantization issues of the belief propagation(BP)algorithm for the quasi-cycle low-density parity code(QC-LDPC)of 802.16estandard are studied in this paper.We consider the quantization problem of input signal and mid-variables from the following three aspects:the variables range,bits and methods of the quantization and make a performance simulation and contrast..Then,a innovative and efficient scheme of quantization is proposed in this paper.The quasi-cycle low-density parity code(QC-LDPC)is used here.It has a better performance and it is beneficial for implementation of hardware.Assuming the input is equiprobable signal and the maximum number of iteration is 10,comparing to the standard(non-quantization)BP algorithm,the proposed scheme keeps the lower bit error rate(BER)by means of the MATLAB simulation meanwhile the hardware complexity is greatly reduced.
The paper presents a novel approach to reduce the bit error rate (BER) in iterative belief propagation (BP) decoding of low density parity check (LDPC) codes. The behavior of the BP algorithm is first investigated as a function of number of decoder iterations, and it is shown that typical uncorrected error patterns can be classified into 3 categories: oscillating, nearly-constant, or random-like, with a predominance of oscillating patterns at high Signal-to-Noise (SNR) values. A proposed decoder modification is then introduced based on tracking the number of failed parity check equations in the intermediate decoding iterations, rather than relying on the final decoder output (after reaching the maximum number of iterations). Simulation results with a rate ¿ (1024,512) progressive edge-growth (PEG) LDPC code show that the proposed modification can decrease the BER by as much as 10-to-40%, particularly for high SNR values.
Summary The simplicity of decoding is one of the most important characteristics of the low density parity check (LDPC) codes. Belief propagation (BP) decoding algorithm is a well‐known decoding algorithm for LDPC codes. Most LDPC codes with long lengths have short cycles in their Tanner graphs, which reduce the performance of the BP algorithm. In this paper, we present 2 methods to improve the BP decoding algorithm for LDPC codes. In these methods, the calculation of the variable nodes is controlled by using “multiplicative correction factor” and “additive correction factor.” These factors are obtained for 2 separate channels, namely additive white Gaussian noise (AWGN) and binary symmetric channel (BSC), as 2 functions of code and channel parameters. Moreover, we use the BP‐based method in the calculation of the check nodes, which reduces the required resources. Simulation results show the proposed algorithm has better performance and lower decoding error as compared to BP and similar methods like normalized‐BP and offset‐BP algorithms.
Many classes of high-performance Low-density parity check codes are based on parity check matrices composed of permutation sub matrices. The emulation-simulation framework further allows the algorithm and implementation to be iteratively redefined to improve the error floor performance of message passing decoder. Log-Like hood-Ratio (LLR) based Belief-Propagation (BP) algorithm is presented for Low Density Parity Check codes. Numerically accurate representation of check node update computation used in LLR-BP decoding is described. The implementation of Sum-Product algorithm (SPA) within Low Density Parity Check Code (LDPC) decoder is described in this paper and the correction term is used to improve the decoding performance of min-sum algorithm (MSA). Quantization and log-tanh function approximation in sum-product decoder strongly affect which absorbing set dominates in error floor region. For LDPC decoder, bit error rate (BER) decreases with increase in the signal to noise ratio.
According to no-convergence of falsely oscillating iteration in the low-density parity-check(LDPC) decoding at the range from medium to high SNRs(Signal to Noise Ratios),we propose a modified LDPC belief propagation(BP) decoding algorithm,i.e.soft value zero-forcing BP algorithm.By setting extrinsic information of the oscillating iteration bit nodes into zero,the impact on the iteratively decoding from false channel information is greatly reduced.And it also improves the performance of the decoding algorithm.Furthermore,a decision criterion of oscillating iteration nodes is presented to increase the accuracy of the decision.Simulation results show that the proposed algorithm has better decoding performance than that of the BP algorithm with the same iterations at medium and high SNRs.