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International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

Improved Algorithm Based on Confidence Propagation Decoding Algorithm

Xiuyang Li, Yanfeng Tang, Cheng Zhang, Xiuzhuo Wang

Published Jun 16, 2023• 0 citations
Publisher

Abstract

Low-density parity-check codes(LDPC) have the advantages of high performance, flexibility, parallelized processing and iterative decoding, which make them one of the widely used error correction code schemes in communication and storage systems. In order to improve the decoding convergence speed of low-density parity-check codes, the idea of layered decoding is added to the log-likelihood ratio confidence propagation decoding algorithm, and an improved algorithm based on layered decoding strategy is proposed, which solves the problem of low decoding rate caused by vertical iteration after the completion of horizontal iteration by using the log-likelihood ratio confidence propagation decoding algorithm in each layer, at the same time. The results show that the average decoding rate of the algorithm is improved by about 25.5% compared with that before the improvement without losing or almost losing the error correction performance, and the performance of the improved layered decoding algorithm with log-likelihood ratio confidence propagation decoding is optimal when the signal-to-noise ratio is greater than 4, and the decoding performance is improved.

DOI
10.1109/ainit59027.2023.10212630

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Low-density parity-check codes(LDPC) have the advantages of high performance, flexibility, parallelized processing and iterative decoding, which make them one of the widely used error correction code schemes in communication and storage systems. In order to improve the decoding convergence speed of low-density parity-check codes, the idea of layered decoding is added to the log-likelihood ratio confidence propagation decoding algorithm, and an improved algorithm based on layered decoding strategy is proposed, which solves the problem of low decoding rate caused by vertical iteration after the completion of horizontal iteration by using the log-likelihood ratio confidence propagation decoding algorithm in each layer, at the same time. The results show that the average decoding rate of the algorithm is improved by about 25.5% compared with that before the improvement without losing or almost losing the error correction performance, and the performance of the improved layered decoding algorithm with log-likelihood ratio confidence propagation decoding is optimal when the signal-to-noise ratio is greater than 4, and the decoding performance is improved.
In order to reduce the bit error rate of low-density parity-check codes for layered decoding algorithm and improve the decoding convergence speed, an improved layered decoding algorithm is proposed. In layered decoding algorithm, updated bit nodes information can be used by check nodes in the iteration process while in dynamic decoding algorithm check nodes update in order according to the reliability. The proposed algorithm combines advantages of the layered and dynamic decoding algorithm. Moreover, compared with the definition of remaining information for check nodes in the dynamic decoding algorithm, a new method to evaluate the reliability is defined in the proposed algorithm. Based on 1/2 code rate china mobile multimedia broadcasting standard, simulation and comparison between the conventional layered decoding algorithm and the proposed layered decoding algorithm have been carried out. The simulation results show that under the same bit error rate, the signal to noise ratio of the improved algorithm reduces nearly 0.4dB as well as convergence speed improves obviously. The proposed layered decoding algorithm reduces the bit error rate and improves the reliability of data in communication system which uses LDPC codes as the channel coding.
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.
Several decoding algorithms based on oscillation Log-likelihood ratio (LLR) for Lowdensity parity-check (LDPC) codes are investigated in this paper, based on which, an improved decoding algorithm which uses the accumulated LLR as the final bit decision merit is proposed. This approach improves the decoding performance with only a very small increase in storage and computation. Combined with normalized belief-propagation (BP), two algorithms are given, which are named as accumulate normalized BP (ACC-Nor-BP) and compare-add normalized BP (CA-Nor-BP). The storage requirements and computational complexity of ACC-Nor-BP is lower than that of CA-Nor-BP and CA-BP, but performance is close to the CA-Nor-BP and better than CA-BP; therefore ACC-Nor-BP algorithm is an effectively improved BP algorithm which has a good balance between performance and complexity.
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.
Low-Density Parity-Check code is a kind of near-optimal error correction code. In order to improve the performance of layered decoding algorithms, two algorithms are put forward in this paper. In the first algorithm, additional messages are employed for the messages updating operation of each layer by using another layered part, making it possible to accelerate the speed of convergence. In the second one, a correction operation for certain type of errors is performed to improve the decoding performance. Simulation results show that more decoding gain and accelerated convergence can be obtained compared with the conventional layered decoding algorithms.
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.