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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Improved Min-Sum-Plus-Correction-Factor Algorithm using stopping node for LDPC decoding

Gang Wang, Nguyen Thi Dieu Linh, Georgia Rugumira

Published Jan 1, 2012• 1 citation
Publisher

Abstract

Low-Density-Parity-Check Code (LDPC) codes have received great attention recently because of the excellent error-correcting capability and optional error correct code. A new method base on Min-Sum-Plus-Correction-Factor Algorithm using stopping node to reduce computing complexity on Low-Density-Parity-Check Code decoding algorithm is presented in this paper. The improved method shows how to make early decisions to reduce computation complexity of LDPC decoding algorithm in the next iteration under QPSK systems. Simulation results show that computational complexity of LDPC decoding using Stopping node criterion can reduce up to 6 times with maintaining quality level.

DOI
10.1109/isspit.2012.6621255

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Low-Density-Parity-Check Code (LDPC) codes have received great attention recently because of the excellent error-correcting capability and optional error correct code. A new method base on Min-Sum-Plus-Correction-Factor Algorithm using stopping node to reduce computing complexity on Low-Density-Parity-Check Code decoding algorithm is presented in this paper. The improved method shows how to make early decisions to reduce computation complexity of LDPC decoding algorithm in the next iteration under QPSK systems. Simulation results show that computational complexity of LDPC decoding using Stopping node criterion can reduce up to 6 times with maintaining quality level.
Low-Density Parity-Check (LDPC) code is a linear block code approaching the Shannon limit. The improvement of the decoding performance of LDPC codes is a hot research direction today. To improve the decoding performance of the self-correcting min-sum (SCMS) algorithm, an improved self-correcting min-sum (ISCMS) algorithm is proposed based on this algorithm. In the iterative decoding process of the ISCMS algorithm, the update rule of the check node (CN) is optimized to the update rule of the CN of the IOMS algorithm, which improves the accuracy of decoding from the processing of the CN. Simulation results show that the proposed ISCMS algorithm has better error performance and convergence performance than the SCMS algorithm with a small increase in computational complexity.
Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.
Low density parity check code is a kind of high-performance linear block code. It has excellent performance which is near the Shannon limit. LDPC has low decoding complexity, free structure and receives extensive attention. The typical decoding algorithm of LDPC is LLR BP algorithm, which is also called “sum-product algorithm”. LLR BP algorithm is the best algorithm at present. But it needs complex computation, which causes great difficulty in its hardware design. To solve this problem, an algorithm that gets approximation of LLR BP algorithm is put forward, which is called min-sum algorithm. Min-sum algorithm greatly reduces the computation and makes the hardware design simpler, but its accuracy has a wide gap with BP algorithm. This article analyzes the reason why min-sum algorithm has errors, and puts forward an improved algorithm called min-sum linear approximation algorithm based on min-sum algorithm.
Abstract ― Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.
This paper proposes a new min-sum algorithm for low-density parity-check decoding. In this paper, we first define the negative and positive effects of the received signal-to-noise ratio (SNR) in the min-sum decoding algorithm. To improve the performance of error correction by considering the negative and positive effects of the received SNR, the proposed algorithm applies adaptive scaling factors not only to extrinsic information but also to a received log-likelihood ratio. We also propose a combined variable and check node architecture to realize the proposed algorithm with low complexity. The simulation results show that the proposed algorithm achieves up to 0.4 dB coding gain with low complexity compared to existing min-sum-based algorithms.