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Современные проблемы науки и образования

Декодирование самоортогональных помехоустойчивых кодов с помощью многопорогового и min-sum алгоритмов

Natalya N. Grinchenko, В Т Као, Gennady V. Ovechkin

Published Jan 1, 2015• 0 citations
OpenAlex

Abstract

It’s discusses the self-orthogonal error-correcting codes (SOC) commonly decoded with multithreshold decoders (MTD). It is shown algorithms used for low-density parity-check codes (LDPC) decoding can be applied for SOC decoding. It is shown that the use of min-sum algorithm for decoding of SOC with code rate 1/2 in a channel with additive white Gaussian noise (AWGN) with binary phase-shift keying and soft demodulation would provide additional coding gain about 1..1,5 dB compared with MTD. Thus, the computational complexity of min-sum algorithm is 6...7 times more than the complexity of MTD. In this paper is proposed the composite decoder comprising elements of MTD and min-sum algorithms for decoding of SOC. Thus on the first few iterations of decoding it is used min-sum decoder and after MTD is used. The simulation results of the proposed decoding scheme show that its use can improve the coding gain compared with MTD approximately 1 dB for SOC with code rate 1/2 over AWGN channel with binary phase-shift keying at cost of doubling in computational complexity. At the same time coding gain depends on the SOC, the number of decoding iterations of min-sum and MTD algorithms.

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Improving performance of multithreshold decoder for self-orthogonal codes

Aleksey I. Baranchikov, Natalya N. Grinchenko, Gennady V. Ovechkin

The article discusses self-orthogonal error-correcting codes (SOC) for the decoding of which multithreshold algorithms (MTD) are usually used. To decode SOC the algorithms used for low-density parity-check (LDPC) codes can also be applied. The article shows that using a min-sum decoder for SOC over a channel with additive white Gaussian noise (AWGN) in case of binary phase shift keying allows to receive additional coding gain about 1..1,5 dB in comparison with MTD usage. At the same time computing complexity in a min-sum algorithm turns out to be 6...7 times higher than MTD. For SOC decoding the work offers a combined decoder including the elements of MTD and min-sum algorithms. The first several decoding iterations require the usage of min-sum decoder while later MTD is added. The results of offered decoder simulation show about 1 dB increase of coding gain in comparison with MTD for SOC over a channel with AWGN with binary phase shift keying in case of twofold increase of computing complexity. The gain received depends on the SOC used, the number of min-sum decoding iterations and MTD.

Mediterranean Conference on Embedded Computing (MECO)•Jun 1, 2016•1 citation

Алгоритм построения самоортогональных кодов для многопороговых декодеров

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An algorithm for self-orthogonal codes (SOC) construction is developed. These codes are applied with multithreshold decoders (MTD) and can provide good error correcting ability at high noise level. This algorithm constructs codes with lower error propagation due to optimizing weight of information and check branches. Such codes allow to perform near optimal decoding at lower signal to noise ratio than codes known earlier.

Известия Тульского государственного университета. Технические науки•Jan 1, 2013•0 citations

Improving performance of multithreshold decoder over binary erasure channel

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The article discusses self-orthogonal error-correcting codes (SOC) for the decoding of which multithreshold algorithms (MTD) are usually used. The work gives the results of the research of MTD efficiency in binary erasure channels. Also new, longer and more efficient convolutional SOCs are received, comparative analysis of MTD efficiency and the efficiency of best methods to recover erasures is made. It is offered new concatenated code consisting of SOC and parity-check code for binary erasure channel allowing to decrease bit unrecovered erasure probability in the area of MTD efficient operation to 2..3 decimal orders. Lower bounds on bit unrecovered erasure probabilities for a decoder of the concatenated code are given; the its simulation results are discussed.

Mediterranean Conference on Embedded Computing (MECO)•Jun 1, 2017•6 citations

The Performance of Multithreshold Decoders in Concatenated Schemes Over Erasure Channels

V. V. Zolotarev, Natalya N. Grinchenko, Gennady V. Ovechkin +1 more

Multithreshold decoders (MTD) of self-orthogonal codes (SOC) for erasure channels implementing error correction methods based on searching global extremum of functions in discrete spaces are considered. To increase the efficiency of erasure recoveries the concatenated codes comprising inner SOC and outer codes being simple for decoding such as parity-check codes, Hamming codes or BCH codes are offered. The usage of the codes offered provides efficient erasure recovery when operating near channel capacity with linear decoder complexity. The questions of high-throughput software MTD implementation recovering erasures using GPU are considered. The MTD versions developed are shown to be able to perform data flow decoding with the rate of several hundred MB/s using GPU.

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Düşük yoğunluklu eşlik denetim kodları için kod çözme algoritmalarının karşılaştırılması.

Kolaylı, Mert

Low-density parity-check (LDPC) codes are a subclass of linear block codes. These codes have parity-check matrices in which the ratio of the non-zero elements to all elements is low. This property is exploited in defining low complexity decoding algorithms. Low-density parity-check codes have good distance properties and error correction capability near Shannon limits. In this thesis, the sum-product and the bit-flip decoding algorithms for low-density parity-check codes are implemented on Intel Pentium M 1,86 GHz processor using the software called MATLAB. Simulations for the two decoding algorithms are made over additive white gaussian noise (AWGN) channel changing the code parameters like the information rate, the blocklength of the code and the column weight of the parity-check matrix. Performance comparison of the two decoding algorithms are made according to these simulation results. As expected, the sum-product algorithm, which is based on soft-decision decoding, outperforms the bit-flip algorithm, which depends on hard-decision decoding. Our simulations show that the performance of LDPC codes improves with increasing blocklength and number of iterations for both decoding algorithms. Since the sum-product algorithm has lower error-floor characteristics, increasing the number of iterations is more effective for the sum-product decoder compared to the bit-flip decoder. By having better BER performance for lower information rates, the bit-flip algorithm performs according to the expectations; however, the performance of the sum-product decoder deteriorates for information rates below 0.5 instead of improving. By irregular construction of LDPC codes, a performance improvement is observed especially for low SNR values.

•Jan 1, 2006•0 citations

Эффективное многопороговое декодирование недвоичных кодов с предварительной оценкой ошибочности проверок

А В Башкиров, Свиридова Ирина Владимировна, А. В. Муратов

This article presents a synthesis of the basic principles of multithreshold decoding (MTD) for non-binary codes. It is shown that the effectiveness of non-binary MTD is close to the results, provides an optimal exhaustive search methods, which for non-binary codes usually unrealizable. The problems of the complexity of proposed decoders

Вестник Воронежского государственного технического университета•Jan 1, 2015•0 citations
It’s discusses the self-orthogonal error-correcting codes (SOC) commonly decoded with multithreshold decoders (MTD). It is shown algorithms used for low-density parity-check codes (LDPC) decoding can be applied for SOC decoding. It is shown that the use of min-sum algorithm for decoding of SOC with code rate 1/2 in a channel with additive white Gaussian noise (AWGN) with binary phase-shift keying and soft demodulation would provide additional coding gain about 1..1,5 dB compared with MTD. Thus, the computational complexity of min-sum algorithm is 6...7 times more than the complexity of MTD. In this paper is proposed the composite decoder comprising elements of MTD and min-sum algorithms for decoding of SOC. Thus on the first few iterations of decoding it is used min-sum decoder and after MTD is used. The simulation results of the proposed decoding scheme show that its use can improve the coding gain compared with MTD approximately 1 dB for SOC with code rate 1/2 over AWGN channel with binary phase-shift keying at cost of doubling in computational complexity. At the same time coding gain depends on the SOC, the number of decoding iterations of min-sum and MTD algorithms.
The article discusses self-orthogonal error-correcting codes (SOC) for the decoding of which multithreshold algorithms (MTD) are usually used. To decode SOC the algorithms used for low-density parity-check (LDPC) codes can also be applied. The article shows that using a min-sum decoder for SOC over a channel with additive white Gaussian noise (AWGN) in case of binary phase shift keying allows to receive additional coding gain about 1..1,5 dB in comparison with MTD usage. At the same time computing complexity in a min-sum algorithm turns out to be 6...7 times higher than MTD. For SOC decoding the work offers a combined decoder including the elements of MTD and min-sum algorithms. The first several decoding iterations require the usage of min-sum decoder while later MTD is added. The results of offered decoder simulation show about 1 dB increase of coding gain in comparison with MTD for SOC over a channel with AWGN with binary phase shift keying in case of twofold increase of computing complexity. The gain received depends on the SOC used, the number of min-sum decoding iterations and MTD.
The article discusses self-orthogonal error-correcting codes (SOC) for the decoding of which multithreshold algorithms (MTD) are usually used. The work gives the results of the research of MTD efficiency in binary erasure channels. Also new, longer and more efficient convolutional SOCs are received, comparative analysis of MTD efficiency and the efficiency of best methods to recover erasures is made. It is offered new concatenated code consisting of SOC and parity-check code for binary erasure channel allowing to decrease bit unrecovered erasure probability in the area of MTD efficient operation to 2..3 decimal orders. Lower bounds on bit unrecovered erasure probabilities for a decoder of the concatenated code are given; the its simulation results are discussed.
Multithreshold decoders (MTD) of self-orthogonal codes (SOC) for erasure channels implementing error correction methods based on searching global extremum of functions in discrete spaces are considered. To increase the efficiency of erasure recoveries the concatenated codes comprising inner SOC and outer codes being simple for decoding such as parity-check codes, Hamming codes or BCH codes are offered. The usage of the codes offered provides efficient erasure recovery when operating near channel capacity with linear decoder complexity. The questions of high-throughput software MTD implementation recovering erasures using GPU are considered. The MTD versions developed are shown to be able to perform data flow decoding with the rate of several hundred MB/s using GPU.
Low-density parity-check (LDPC) codes are a subclass of linear block codes. These codes have parity-check matrices in which the ratio of the non-zero elements to all elements is low. This property is exploited in defining low complexity decoding algorithms. Low-density parity-check codes have good distance properties and error correction capability near Shannon limits. In this thesis, the sum-product and the bit-flip decoding algorithms for low-density parity-check codes are implemented on Intel Pentium M 1,86 GHz processor using the software called MATLAB. Simulations for the two decoding algorithms are made over additive white gaussian noise (AWGN) channel changing the code parameters like the information rate, the blocklength of the code and the column weight of the parity-check matrix. Performance comparison of the two decoding algorithms are made according to these simulation results. As expected, the sum-product algorithm, which is based on soft-decision decoding, outperforms the bit-flip algorithm, which depends on hard-decision decoding. Our simulations show that the performance of LDPC codes improves with increasing blocklength and number of iterations for both decoding algorithms. Since the sum-product algorithm has lower error-floor characteristics, increasing the number of iterations is more effective for the sum-product decoder compared to the bit-flip decoder. By having better BER performance for lower information rates, the bit-flip algorithm performs according to the expectations; however, the performance of the sum-product decoder deteriorates for information rates below 0.5 instead of improving. By irregular construction of LDPC codes, a performance improvement is observed especially for low SNR values.