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Journal of Communications

Min-sum decoding algorithm for LDPC codes based on integer operation

Sen Li

Published Jan 1, 2010• 1 citation
OpenAlex

Abstract

The LDPC codes decoding algorithm over additive white gaussian noise(AWGN) channels was studied,and a min-sum algorithm based on integer operation was proposed for decoding LDPC codes.All the variables were fixed length integers in this algorithm,which was very convenient for hardware implementation.At the signal-to-noise ratios(SNR) above 3dB,its performance was equal to that of sum-product algorithm whose variables were high precision floating point numbers.Also,a coefficient was designed to modify the min-sum algorithm,whose decoding performance was improved greatly especially at low SNR.And another achievement had been found that this algorithm had a lower error floor at high SNR.

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This paper analyzes the performance of quantized min-sum decoding algorithms for irregular low-density parity-check (LDPC) codes. For regular LDPC codes, it is known that the normalized or offset min-sum decoding algorithm with quantization bits less than 6 bits achieves good performances over wide range of signal-to-noise ratios (SNR). However, finite precision effects in decoding irregular LDPC codes are different from that in decoding regular LDPC codes which is caused by the difference of convergence speeds between low degree nodes and high degree nodes. This paper proposes a novel method to improve the performance of the conventional normalized or offset min-sum decoding algorithm when it is approximated with finite precision for hardware implementations. The proposed method applies down-scaling factors to intrinsic information which has effects on increasing the reliability of extrinsic information at variable nodes and compensating the quantization errors caused by finite precision. Computer simulation results for irregular LDPC codes show that the proposed normalized and offset min-sum decoding algorithms achieve better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.

IEEE International Symposium on Circuits and Systems proceedings•May 1, 2007•15 citations
The LDPC codes decoding algorithm over additive white gaussian noise(AWGN) channels was studied,and a min-sum algorithm based on integer operation was proposed for decoding LDPC codes.All the variables were fixed length integers in this algorithm,which was very convenient for hardware implementation.At the signal-to-noise ratios(SNR) above 3dB,its performance was equal to that of sum-product algorithm whose variables were high precision floating point numbers.Also,a coefficient was designed to modify the min-sum algorithm,whose decoding performance was improved greatly especially at low SNR.And another achievement had been found that this algorithm had a lower error floor at high SNR.
Low-Density Parity-Check(LDPC) codes are typical of preeminent performance approaching Shannon’s capacity limit.This paper studies the LDPC codes min-sum decoding algorithm,and a min-sum algorithm in QPSK modulation based on integer operation is proposed for decoding LDPC codes.All the variables are fixed length integers in this algorithm,so it is very convenient for hardware implementation.Simulation results show that its performance is close to that of sum-product algorithm whose variables are high precision floating point numbers.
In this paper,several conventional decoding algorithms of LDPC(Low-Density Parity-Check) codes over Additive White Gaussian Noise(AWGN) channels are investigated,and an improved decoding algorithm based on integer operation is proposed. Compared to normalized min-sum algorithm,a modified factor to improve the decoding performance is designed according to the states of hard decision in each iteration. It is convenient for hardware implementation with integer operation.The simulation results show that the algorithm performs better than LLR BP decoding algorithm in 2.1 dB and decodes fast with low complexity.
Based on the investigation of the low-density parity-check(LDPC) codes' layered decoding algorithm in additive white Gaussian noise(AWGN) channel,an improved layered decoding algorithm based on integer operation is proposed for decoding LDPC codes.All the variables are indicated as integers in the proposed algorithm,so it is very convenient for implementation in hardware.At the same time,a modified factor is introduced to improve the layered decoding algorithm.With this modified factor,its decoding performance is improved greatly.Simulation results in AWGN channel show that the improved layered decoding algorithm can not only reduce the computation complexity and accelerate the convergence rate,but also have a lower error floor.
It's common to use high-precision floating-point operations on theoretical study of the Low Density Parity Check (LDPC) codes, but it is difficult to implement it in practical application. Therefore, there is a great need to design effective quantitative method which enables us to implement it in hardware easily. In this paper, the min-sum decoding algorithm of LDPC codes for 8PSK modulation is studied in detail, and the method of the uniform quantization is analyzed and deduced. With this method to quantify the initial message, the variable messages and check messages are converted into integers after each iteration. Then the min-sum decoding algorithm based on integer arithmetic for 8PSK modulation is implemented. Simulation results demonstrate that the performance of the min-sum decoding algorithm with the uniform quantization is close to that of the high-precision floating-point sum-product decoding algorithm. Meanwhile, all the variables in the algorithm are fixed-length integers, so it is convenient for hardware implementation.
This paper proposes an adaptive normalized min-sum algorithm for the decoding of low-density parity check (LDPC) codes, which utilizes an adaptive normalization factor to improve the accuracy of the soft information transferred during the iterative decoding process, and provides superior performance accordingly. This adaptive normalization factor can be adjusted dynamically and adaptively at each decoding iteration according to a look-up table obtained via training and simulation. Its implementation facility and independence from the channel characteristics make the proposed adaptive normalized min-sum algorithm expect a wide application. Simulation results show that the proposed algorithm can achieve performance much closer to the sum-product algorithm and a coding gain of around 0.2dB compared to the conventional normalized min-sum algorithm for DVB-S2's rate 2/5 and 3/5 LDPC codes over the additive white Gaussian noise (AWGN) channel.
This paper analyzes the performance of quantized min-sum decoding algorithms for irregular low-density parity-check (LDPC) codes. For regular LDPC codes, it is known that the normalized or offset min-sum decoding algorithm with quantization bits less than 6 bits achieves good performances over wide range of signal-to-noise ratios (SNR). However, finite precision effects in decoding irregular LDPC codes are different from that in decoding regular LDPC codes which is caused by the difference of convergence speeds between low degree nodes and high degree nodes. This paper proposes a novel method to improve the performance of the conventional normalized or offset min-sum decoding algorithm when it is approximated with finite precision for hardware implementations. The proposed method applies down-scaling factors to intrinsic information which has effects on increasing the reliability of extrinsic information at variable nodes and compensating the quantization errors caused by finite precision. Computer simulation results for irregular LDPC codes show that the proposed normalized and offset min-sum decoding algorithms achieve better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.