• ETRI Journal
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• v.34 no.4
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• pp.629-632
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• 2012

This letter investigates the combination of the Chase-2 and sum-product (SP) algorithms for low-density parity-check (LDPC) codes. A simple modification of the tanh rule for check node update is given, which incorporates test error patterns (TEPs) used in the Chase algorithm into SP decoding of LDPC codes. Moreover, a simple yet effective approach is proposed to construct TEPs for dealing with decoding failures with low-weight syndromes. Simulation results show that the proposed algorithm is effective in improving both the waterfall and error floor performance of LDPC codes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.322-328
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• 2009

This paper proposes new simplified sum-product (SSP) decoding algorithm to improve BER performance for low-density parity-check codes. The proposed SSP algorithm can replace multiplications and divisions with additions and subtractions without extra computations. In addition, the proposed SSP algorithm can simplify both the In[tanh(x)] and tanh-1 [exp(x)] by using two quantization tables which can reduce tremendous computational complexity. Moreover, the simulation results show that the proposed SSP algorithm can improve about $0.3\;{\sim}\;0.8\;dB$ of BER performance compared with the existing modified sum-product algorithms.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1262-1270
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• 2017

Among various decoding algorithms of low-density parity-check (LDPC) codes, the min-sum (MS) algorithm and its modified algorithms are widely adopted because of their computational simplicity compared to the sum-product (SP) algorithm with slight loss of decoding performance. In the MS algorithm, the magnitude of the output message from a check node (CN) processing unit is decided by either the smallest or the next smallest input message which are denoted as min1 and min2, respectively. It has been shown that multiplying a scaling factor to the output of CN message will improve the decoding performance. Further, Zhong et al. have shown that multiplying different scaling factors (called a 2-dimensional scaling) to min1 and min2 much increases the performance of the LDPC decoder. In this paper, the simplified 2-dimensional scaled (S2DS) MS algorithm is proposed. In the proposed algorithm, we figure out a pair of the most efficient scaling factors which multiplications can be replaced with combinations of addition and shift operations. Furthermore, one scaling operation is approximated by the difference between min1 and min2. The simulation results show that S2DS achieves the error correcting performance which is close to or outperforms the SP algorithm regardless of coding rates, and its computational complexity is the lowest comparing to modified versions of MS algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1892-1897
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• 2012

In this paper, efficient CNU(Check Node Update) algorithms are analyzed for high speed LDPC decoding in DVB-S2 standard. In aspect to CNU methods, there are some kinds of CNU methods. Among of them, MP (Min Product) method is quite often used in LDPC decoding. However MP needs LUT (Look Up Table) that is critical path in LDPC decoding speed. A new SC-NMS (Self-Corrected Normalized Min-Sum) method is proposed in the paper. NMS needs only normalized scaling factor instead of LUT and compensates the overestimation of MP approximation. In addition, SC method is proposed. It gives a faster convergence toward a decoded codeword. If a message change its sign between two iterations, it is not reliable and to avoid to propagate noisy information, its module is set to 0. The performance of SC-NMS has a little degrade compare to MP by 0.1 dB, however considering computational complexity and decoding speed, SC-NMS algorithm is optimal method for CNU algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.11
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• pp.1248-1255
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• 2012

LDPC decoder architectures are generally classified into serial, parallel and partially parallel architectures. Conventional method of LDPC decoding in general give rise to a large number of computation operations, mass power consumption, and decoding delay. It is necessary to reduce the iteration numbers and computation operations without performance degradation. This paper studies horizontal shuffle scheduling(HSS) algorithm and self-correction normalized min-sum(SC-NMS) algorithm. In the result, number of iteration is half than conventional algorithm and performance is almost same between sum-product(SP) and SC-NMS. Finally, This paper implements high-speed LDPC decoder based on FPGA. Decoding throughput is 816 Mbps.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.129-137
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• 2001

Matching pursuit algorithm was successfully demonstrated useful in low bit-rate video coding, However, one of the practical concerns related to applying the matching pursuit algorithm to application is its massive computation required for finding bases whose weighted sum best approximates the given input image. The main contribution of this paper is that we provide a new method that can drastically reduce the computational load without any degradation of image quality. Its main idea is based on reducing the number of inner product calculation required for finding best bases because the complexity of matching pursuit algorithm is due to the exhaustive local inner product calculation. As the first step, we compute a matrix which is the 1-D inner product of the given motion-compensated error input image with the 1-D vertical Gabor functions using the separable property of Gabor bases. In the second step, we calculate length of each vector in the matrix that corresponds to 1-D horizontal Gabor function, and compare the length with the current maximum absolute inner product value so far. According to the result of this comparison, one can decide whether or not to calculate the inner product. Since most of them do not need to calculate the inner product value, one can significantly reduce the computational load. Experimental results show that proposed method reduces about 70% of inner product calculation compared to the Neff's fast algorithm without any degradation of image quality.