Analysis of efficiency of the bioinspired method for decoding algebraic convolutional codes
Journal Title: Восточно-Европейский журнал передовых технологий - Year 2019, Vol 2, Issue 4
Abstract
<p>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.</p><p>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.</p><p>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.</p>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
Authors and Affiliations
Sergii Panchenko, Mykola Shtompel, Viktor Kosenko, Oleksandr Shefer, Olha Dunaievska
Keywords
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- EP ID EP666297
- DOI 10.15587/1729-4061.2019.160753
- Views 86
- Downloads 0
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