Analysis of efficiency of the bioinspired method for decoding algebraic convolutional codes

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.160753

Keywords:

wireless telecommunication systems, convolutional codes, algebraic structure, decoding, bioinspired search

Abstract

It has been shown that convolutional codes are widely used, along with various decoding methods, to improve the reliability of information transmission in wireless telecommunication systems. The general principles of synthesis and the parameters and algebraic non-systematic convolutional codes with arbitrary coding rate and maximum achievable code distance have been shown.

The basic stages of the bioinspired method for decoding algebraic convolutional codes using a random shift mechanism have been presented. It has been shown that the essence of the presented decoding method implies applying the procedure of differential evolution with the heuristically determined parameters. In addition, this method uses information about the reliability of the adopted symbols to find the most reliable basis for the generalized generator matrix. The mechanism of random shift for the modification of the accepted sequence is additionally applied for the bioinspired search based on various most reliable bases of a generalized generator matrix.

The research results established that the bioinspired method for decoding algebraic convolutional codes ensures greater efficiency compared with the algebraic decoding method in the communication channel with additive white Gaussian noise. Depending on the parameters of the algebraic convolutional code and the necessary error coefficient, the energy gain from encoding ranges from 1.6 dB to 3 dB. It was shown that the presented bioinspired decoding method can be used for convolutional codes with a large code constraint length.

In doing so, the presented method for decoding algebraic convolutional codes is less efficient than the Viterbi decoding method and turbo codes at a sufficient number of decoding iterations

Author Biographies

Sergii Panchenko, Ukrainian State University of Railway Transport Feierbakh sq., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Professor, Rector

Department of Automation and Computer Remote Control Train Traffic

Sergii Prykhodko, Ukrainian State University of Railway Transport Feierbakh sq., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Professor, Vice-Rector

Department of Transport Communication

Sergii Kozelkov, Educational and Research Institute of Telecommunications and Informatization State University of Telecommunications Solomianska str., 7, Kyiv, Ukraine, 03110

Doctor of Technical Sciences, Professor, Director

Mykola Shtompel, Ukrainian State University of Railway Transport Feierbakh sq., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Associate Professor

Department of Transport Communication

Viktor Kosenko, State Enterprise «Kharkіv Scientific-Research Institute of Mechanical Engineering Technology» Krivokonivska str., 30, Kharkiv, Ukraine, 61016

Doctor of Technical Sciences, Associate Professor, Director

Oleksandr Shefer, Poltava National Technical Yuri Kondratyuk University Pershotravnevyi ave., 24, Poltava, Ukraine, 36011

Doctor of Technical Sciences, Associate Professor

Department of Automation and Electric Drive

Olha Dunaievska, National Technical University «Kharkiv Polytechnic Institute» Kyrpychova str., 2, Kharkiv, Ukraine, 61002

PhD

Department of Computer Mathematics and Data Analysis

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Published

2019-03-25

How to Cite

Panchenko, S., Prykhodko, S., Kozelkov, S., Shtompel, M., Kosenko, V., Shefer, O., & Dunaievska, O. (2019). Analysis of efficiency of the bioinspired method for decoding algebraic convolutional codes. Eastern-European Journal of Enterprise Technologies, 2(4 (98), 22–30. https://doi.org/10.15587/1729-4061.2019.160753

Issue

Vol. 2 No. 4 (98) (2019): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2019 Sergii Panchenko, Sergii Prykhodko, Sergii Kozelkov, Mykola Shtompel, Viktor Kosenko, Oleksandr Shefer, Olha Dunaievska

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