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IEEE International Conference on Communication Technology (ICCT)

LLR Distribution Characteristics Based Hybrid Decoding Algorithm for LDPC Codes

Renzhi Wang, Kegang Pan, Xinting Wang

Published Oct 28, 2020• 1 citation
Publisher

Abstract

To improve the decoding speed and reduce the complexity of low-density parity-check (LDPC) codes, this paper proposes a hybrid LDPC decoding algorithm that combined with the distribution characteristics of log-likelihood ratio (LLR) of all variable node information before each iteration. During the process of decoding, there is a judgment based on an interval value and a threshold value to select the Belief Propagation (BP) algorithm or the Minimal Sum (MS) algorithm to decode, so the proposed hybrid algorithm effectively combines the excellent performance of BP algorithm and the simple operation of MS algorithm. The simulation results show that the algorithm can greatly reduce the computational complexity and achieve similar decoding performance compared with the BP algorithm.

DOI
10.1109/icct50939.2020.9295754

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To improve the decoding speed and reduce the complexity of low-density parity-check (LDPC) codes, this paper proposes a hybrid LDPC decoding algorithm that combined with the distribution characteristics of log-likelihood ratio (LLR) of all variable node information before each iteration. During the process of decoding, there is a judgment based on an interval value and a threshold value to select the Belief Propagation (BP) algorithm or the Minimal Sum (MS) algorithm to decode, so the proposed hybrid algorithm effectively combines the excellent performance of BP algorithm and the simple operation of MS algorithm. The simulation results show that the algorithm can greatly reduce the computational complexity and achieve similar decoding performance compared with the BP algorithm.
Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.
Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives.
Low-Density Parity check (LDPC) codes offer high-performance error correction near the Shannon limit which employs large code lengths and some iterations in the decoding process. The conventional decoding algorithm of LDPC is the Log Likelihood Ratio based Belief Propagation (LLR BP) which is also known as the `Sum-Product algorithm' which gives the best decoding performance and requires the most computational complexity and implementations with increased hardware complexity. Another simpler variant of this algorithm is used which is known as `min-sum algorithm' which reduces computational complexity as well as hardware complexity but with reduced accuracy. This paper analyzes the reason min-sum algorithm is more prone to errors when compared to the sum-product algorithm, and puts forward two improved algorithms which improve the performance of the min-sum algorithm with comparable algorithmic complexity.
At present, Low-Density Parity-Check (LDPC) codes widely used in many fields of communications have the best performance of all the Error Correcting Codes (ECC). This paper mainly studies the decoding algorithms of LDPC. It proposes an improved algorithm which is named Check-Variable nodes Hybrid(CVH) algorithm on the basis of the existing algorithms. The CVH algorithm can reduce the computational complexity during the check-node update while overcome with the correlation between the variable-node news in a code with circles. As well as, comparing with the original algorithms the performance of the new one saves 0.1 and 0.3 dB than Log-likelihood Ratios (LLR) Belief Propagation (BP) and BP - based algorithms under Additive White Gaussian Noise (AWGN) channel when the Bit Error Rate (BER) falls to through the simulation. This point shows that this algorithm can increase the decoding performance and reduce the error rate effectively.
In iterations processing of LLR BP algorithm for LDPC decoding, the computational complexity of check nodes renewal is high; The decoding performance will be reduced, while there are lots of loops in short LDPC codes. According to these problems, a new LLR BP decoding algorithm based on Hybrid Check-Variable offset processing is proposed in this paper. Decrease complexity by adjusting amplitudes of check nodes processing and relativity of variable nodes by adjusting their information. Simulation results show that this improved decoding algorithm, compared with LLR BP algorithm, is consideration to decoding performance of LDPC and computational complexity.
In iterations processing of LLR BP algorithm for LDPC decoding,the computational of check nodes renewal is high;The decoding performance will be reduced,while there are lots of loops in short LDPC codes.According to these problems,a new LLR BP decoding algorithm based on Hybrid Check-Variable offset processing is proposed in this paper.Decrease complexity by adjusting amplitudes of check nodes processing and relativity of variable nodes by adjusting their information.Simulation results show that this improved decoding algorithm,compared with LLR BP algorithm,is consideration to decoding performance of LDPC and computational complexity.