• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.7
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• pp.1-8
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• 2008

Low Density Parity Check codes(LDPC) are recently focused on communication systems due to its good performance. The standard of WiBro has also included LDPC codes as a channel coding. The weak point of implementation for LDPC encoder is that conventional binary Matrix Vector Multiplier has many clock cycles which limit throughput. In this paper, we propose semi-parallel architecture by using cyclic shift registers and exclusive-OR without conventional Matrix Vector Multipliers over the standard parity check matrices with Circulant Permutation Matrices(CPM). Furthermore, multi-rate encoder is designed by using proposed architecture. Our encoder with multi-rate for IEEE 802.16e LDPC has lower clock cycles and higher throughput.

• ETRI Journal
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• v.27 no.5
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• pp.557-562
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• 2005

Low-density parity-check (LDPC) codes have recently emerged due to their excellent performance. However, the parity check (H) matrices of the previous works are not adequate for hardware implementation of encoders or decoders. This paper proposes a hybrid parity check matrix which is efficient in hardware implementation of both decoders and encoders. The hybrid H-matrices are constructed so that both the semi-random technique and the partly parallel structure can be applied to design encoders and decoders. Using the proposed methods, the implementation of encoders can become practical while keeping the hardware complexity of the partly parallel decoder structures. An encoder and a decoder are designed using Verilog-HDL and are synthesized using a $0.35 {\mu}m$ CMOS standard cell library.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.36-47
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• 2020

The improved belief-propagation-based algorithms, such as normalized min-sum algorithm (NMSA) or offset min-sum algorithm (OMSA), are widely used to decode LDPC(Low-Density Parity-Check) codes because they are less computationally complex and work well even at low SNR(Signal-to-Noise Ratio). However, these algorithms work well only when an appropriate normalization factor or offset value is used. A new method that uses a CMD(Check Node Message Distribution) chart and least-square method, which has been recently proposed, has advantages on computational complexity over other approaches to get optimal coefficients. Furthermore, this method can be used to derive coefficients for each iteration. In this paper, we apply this method and propose an algorithm to derive a combination of normalization factor and offset value for a combined normalized and offset min-sum algorithm to further improve the decoding of LDPC codes. Simulations on the next-generation broadcasting standards, ATSC 3.0 LDPC codes, prove that a combined normalized and offset min-sum algorithm which takes the proposed coefficients as correction coefficients shows the best BER performance among other decoding algorithms.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.639-644
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• 2014

In low-density parity-check (LDPC) coded multiple-input multiple-output (MIMO) communication systems, probabilistic information are exchanged between an LDPC decoder and a MIMO detector. TheMIMO detector has to calculate probabilistic values for each bit which can be very complex. In [1], the authors presented a class of linear block codes named low-density MIMO codes (LDMC) which can reduce the complexity of MIMO detector. However, this code only supports the outer-iterations between the MIMO detector and decoder, but does not support the inner-iterations inside the LDPC decoder. In this paper, a new approach to construct LDMC codes is introduced. The new LDMC codes can be encoded efficiently at the transmitter side and support both of the inner-iterations and outer-iterations at the receiver side. Furthermore they can achieve the design rates and perform very well over MIMO channels.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.210-219
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• 2017

This paper proposes a modified min-max algorithm (MMMA) for nonbinary quasi-cyclic low-density parity-check (NB-QC-LDPC) codes and an efficient parallel block-layered decoder architecture corresponding to the algorithm on a graphics processing unit (GPU) platform. The algorithm removes multiplications over the Galois field (GF) in the merger step to reduce decoding latency without any performance loss. The decoding implementation on a GPU for NB-QC-LDPC codes achieves improvements in both flexibility and scalability. To perform the decoding on the GPU, data and memory structures suitable for parallel computing are designed. The implementation results for NB-QC-LDPC codes over GF(32) and GF(64) demonstrate that the parallel block-layered decoding on a GPU accelerates the decoding process to provide a faster decoding runtime, and obtains a higher coding gain under a low $10^{-10}$ bit error rate and low $10^{-7}$ frame error rate, compared to existing methods.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.5-9
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• 2008

Researchers have investigated many upper bound techniques applicable to error probabilities on the maximum likelihood (ML) decoding performance of turbo-like codes and low density parity check (LDPC) codes in recent years for a long codeword block size. This is because it is trivial for a short codeword block size. Previous research efforts, such as the simple bound technique [20] recently proposed, developed upper bounds for LDPC codes and turbo-like codes using ensemble codes or the uniformly interleaved assumption. This assumption bounds the performance averaged over all ensemble codes or all interleavers. Another previous research effort [21] obtained the upper bound of turbo-like code with a particular interleaver using a truncated union bound which requires information of the minimum Hamming distance and the number of codewords with the minimum Hamming distance. However, it gives the reliable bound only in the region of the error floor where the minimum Hamming distance is dominant, i.e., in the region of high signal-to-noise ratios. Therefore, currently an upper bound on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix cannot be calculated because of heavy complexity so that only average bounds for ensemble codes can be obtained using a uniform interleaver assumption. In this paper, we propose a new bound technique on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix using ML estimated weight distributions and we also show that the practical iterative decoding performance is approximately suboptimal in ML sense because the simulation performance of iterative decoding is worse than the proposed upper bound and no wonder, even worse than ML decoding performance. In order to show this point, we compare the simulation results with the proposed upper bound and previous bounds. The proposed bound technique is based on the simple bound with an approximate weight distribution including several exact smallest distance terms, not with the ensemble distribution or the uniform interleaver assumption. This technique also shows a tighter upper bound than any other previous bound techniques for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.35-39
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• 2011

Error correcting codes with easy variability in code rate and codeword length in addition to powerful error correcting capability are required for present and future mobile multimedia communication systems. And low complexity is also needed for the compact mobile terminals. In general, the irregular random LDPC(low-density parity-check) code is known to have the superior performance among various LDPC codes. But it has inefficiency since the various parity check matrices for various services should be stored for encoding and decoding. The structured LDPC codes which can easily provide various rates and lengths are studied recently. Therefore, the flexibility, memory size, and error performance of various structured LDPC codes are compared and analyzed in this paper. And the most appropriate structured LDPC code is also suggested.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1A
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• pp.34-42
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• 2012

Low density parity check (LDPC) code is widely used, since it shows superior performance close to Shannon limit and its decoding complexity is lower than turbo code. Recently, it is used as a channel code to decode Wyner-Ziv frames in distributed video coding (DVC) system. In this paper, we propose an efficient method to design the parity check matrix H of LDPC codes. In order to apply LDPC code to DVC system, the LDPC code should have rate compatibility. Thus, we also propose a method to merge check nodes of LDPC code to attain the rate compatibility. LDPC code is designed using ACE algorithm and check nodes are merged for a given code rate to maximize the error correction capability. The performance of the designed LDPC code is analyzed extensively by computer simulations.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.406-410
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• 2010

This paper presents an approach to the construction of non-binary quasi-cyclic (QC) low-density parity-check (LDPC) codes based on multiplicative groups over one Galois field GF(q) and Euclidean geometries over another Galois field GF($2^S$). Codes of this class are shown to be regular with girth $6{\leq}g{\leq}18$ and have low densities. Finally, simulation results show that the proposed codes perform very wel with the iterative decoding.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.637-642
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• 2014

In this paper, we propose a efficient shift index searching algorithm for design of the block LDPC codes. It is combined with the message-passing based cycle search algorithm and ACE algorithm. We can determine the shift indices by ordering of priority factors which are effect on the LDPC code performance. Using this algorithm, we can construct the LDPC codes with low complexity compare to trellis-based search algorithm and save the memory for storing the parity check matrix.