• Journal of the Korea Society for Simulation
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• v.14 no.4
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• pp.87-93
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• 2005

In this paper we propose a new iterative decoding method of turbo code which computes the log-likelihood ratios at each MAP (maximum a posteriori) decoder in parallel in each iteration step and combines them with proper weights to produce better decisions. Our results indicate that the proposed decoding method is particularly useful for systems with limited number of iterations and low code rates.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.503-510
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• 2016

In this paper I analyzed two decoding algorithms of decentralized LT codes for distributed storage by simulations in small-scale wireless sensor network. From the simulation results we can see that when the decoding ration is above 2.0, the successful decoding probability of OBP decoding is about 99%, while that of BP decoding is below 50% with n=100, and about 70% with n=200. We showed that OBP decoding algorithm is an efficient decoding scheme for decentralized LT codes for distributed storage in small-scale wireless sensor network.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.1-5
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• 2008

In this paper, we propose and analyze the AMC (Adaptive Modulation and Coding) system with efficient turbo coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique. The proposed algorithm adopts extrinsic information from a MAP (Maximum A Posteriori) decoder with iterative decoding as a priori probability in two decoding procedures of V-BLAST scheme; the ordering and the slicing. Also, we consider the AMC system using the conventional turbo coded V-BLAST technique that simply combines the V-BLAST scheme with the turbo coding scheme. And we compare the proposed decoding algorithm to a conventional V-BLAST decoding algorithm and a ML (Maximum Likelihood) decoding algorithm. In addition, we apply a STD (Selection Transmit Diversity) scheme to the systems for better performance improvement. Results indicate that the proposed systems achieve better throughput performance than the conventional systems over the entire SNR range. In terms of transmission rate performance, the suggested system is close in proximity to the conventional system using the ML decoding algorithm.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.213-220
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• 2015

In this paper, we present a reliability-based iterative proportionality-logic decoding algorithm for two classes of structured low-density parity-check (LDPC) codes. The main contributions of this paper include: 1) Syndrome messages instead of extrinsic messages are processed and exchanged between variable nodes and check nodes, which can reduce the decoding complexity; 2) a more flexible decision mechanism is developed in which the decision threshold can be self-adjusted during the iterative process. Such decision mechanism is particularly effective for decoding the majority-logic decodable codes; 3) only part of the variable nodes satisfying the pre-designed criterion are involved for the presented algorithm, which is in the proportionality-logic sense and can further reduce the computational complexity. Simulation results show that, when combined with factor correction techniques and appropriate proportionality parameter, the presented algorithm performs well and can achieve fast decoding convergence rate while maintaining relative low decoding complexity, especially for small quantized levels (3-4 bits). The presented algorithm provides a candidate for those application scenarios where the memory load and the energy consumption are extremely constrained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.357-359
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• 2010

The Viterbi Decoding is one of the most researched areas of the convolutional decoding methods. In this paper, we use various parameters for the substantial Viterbi decoding and discuss some viterbi decoding methods. And, the viterbi algorithms of the methods, we discuss 'Hard Decision' and 'Soft Decision'. So, we compare differences of two methods about decoding methods, performance. Because of having various parameters and decision methods, we discuss the values of various parameter and decision methods in the Gaussian channel about the viterbi decoding methods.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.289-296
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• 1999

In this paper, an improved fractal color image decoding method using the data dependence parts and the vector distortion measure is proposed. The vector distortion measure exploits the correlation between different color components. The pixel in RGB color space can be considered as a 30dimensional vector with elements of RGB components. The root mean square error(rms) in RGB color for similarity measure of two blocks R and R' was used. We assume that various parameter necessary in image decoding are stored in the transform table. If the parameter is referenced in decoding image, then decoding is performed by the recursive decoding method. If the parameter is not referenced in decoding image, then the parameters recognize as the data dependence parts and store its in the memory. Non-referenced parts can be decoded only one time, because its domain informations exist in the decoded parts by the recursive decoding method. Non-referenced parts are defined the data dependence parts. Image decoding method using data dependence classifies referenced parts and non-referenced parts using information of transform table. And the proposed method can be decoded only one time for R region decoding speed than Zhang & Po's method, since it is decreased the computational numbers by execution iterated contractive transformations for the referenced range only.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.742-748
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• 2010

Instead of using sphere decoding, list sphere decoding (LSD) has been introduced to increase the reliability of log-likelihood ratio (LLR) in recent soft decoding schemes employing iterative detection and decoding (IDD). Although LSD provides improved performance, it does not obtain complexity gain due to signal-to-noise ratio (SNR) increment as it detects large number of lattice points. Especially, its inefficient scenario arises when it has to search for lattice points which have small affect for obtaining LLR with high reliability. In this paper, we study an efficient algorithm to remove such lattice points, which results in complexity reduction based on radius optimization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.180-187
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• 2002

The decoding method using covering polynomials is an extended form of error-trapping decoding, and is a simple and effective means to implement decoders for cyclic codes. Covering polynomials can be used for soft-decision decoding as well as for decoding beyond the bounded distance of the code. The implementation complexity is proportional to the number of covering polynomials employed. In this paper, the soft-decision decoding procedure using covering polynomials is described, and the procedure is applied to the [23,12] Golay code. A new set of covering polynomials is derived for the procedure, which is presented as a generalized closed-form solution. The set can be efficiently utilized for decoding a class of cyclic codes including the Golay code. Computer simulation of the described procedure is performed to show the trade-offs between the decoder performance and complexity. It is demonstrated that soft-decision decoding of the Golay code using the derived set of covering polynomials has less than 0.2dB deviation from the optimal performance of maximum-likelihood decoding, with a reduced complexity when compared to the Chase Algorithm 2 combined with hard-decision decoding that has nearly identical performance.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.635-646
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• 2011

In distributed video coding, low complexity encoder can be realized by shifting encoder-side complex processes to decoder-side. However, not only motion estimation/compensation processes but also complex LDPC decoding process are imposed to the Wyner-Ziv decoder, therefore decoder-side complexity has been one important issue to improve. LDPC decoding process consists of numerous iterative decoding processes, therefore complexity increases as the number of iteration increases. This iterative LDPC decoding process accounts for more than 60% of whole WZ decoding complexity, therefore it can be said to be a main target for complexity reduction. Previously, HDA (Hard Decision Aided) method is introduced for fast LDPC decoding process. For currently received parity bits, HDA method certainly reduces the complexity of decoding process, however, LDPC decoding process is still performed even with insufficient amount of parity request which cannot lead to successful LDPC decoding. Therefore, we can further reduce complexity by avoiding the decoding process for insufficient parity bits. In this paper, therefore, a parity request estimation method is proposed using bit plane-wise correlation and temporal correlation. Joint usage of HDA method and the proposed method achieves about 72% of complexity reduction in LDPC decoding process, while rate distortion performance is degraded only by -0.0275 dB in BDPSNR.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.961-967
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• 2014

Raptor codes are a class of rateless codes originally designed for binary erasure channels. This paper presents a compact set of mathematical expressions for iterative soft decoding of raptor codes. In addition, an early termination scheme is employed, and it is embedded in a single algorithm with the formula. In the proposed algorithm, the performance is enhanced by adopting iterative decoding, both in each inner and outer code and in the concatenated code itself between the inner and outer codes. At the same time, the complexity is reduced by applying an efficient early termination scheme. Simulation results show that our proposed method can achieve better performance with reduced decoding complexity compared to the conventional schemes.