ANALYSIS OF EFFICIENCY OF THE BIOINSPIRED METHOD FOR DECODING ALGEBRAIC CONVOLUTIONAL CODES
Creators
- 1. Ukrainian State University of Railway Transport
- 2. Educational and Research Institute of Telecommunications and Informatization State University of Telecommunications
- 3. State Enterprise «Kharkіv Scientific-Research Institute of Mechanical Engineering Technology»
- 4. Poltava National Technical Yuri Kondratyuk University
- 5. National Technical University «Kharkiv Polytechnic Institute»
Description
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
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References
- Johannesson, R., Zigangirov, K. Sh. (Eds.) (2015). Fundamentals of convolutional coding. John Wiley & Sons, 668. doi: https://doi.org/10.1002/9781119098799
- Ryan, W., Lin, S. (2009). Channel codes: Classical and modern. Cambridge University Press, 692. doi: https://doi.org/10.1017/cbo9780511803253
- Piret, P. (1976). Structure and constructions of cyclic convolutional codes. IEEE Transactions on Information Theory, 22 (2), 147–155. doi: https://doi.org/10.1109/tit.1976.1055531
- Roos, C. (1979). On the structure of convolutional and cyclic convolutional codes. IEEE Transactions on Information Theory, 25 (6), 676–683. doi: https://doi.org/10.1109/tit.1979.1056108
- Gluesing-Luerssen, H., Schmale, W. (2004). On Cyclic Convolutional Codes. Acta Applicandae Mathematicae, 82 (2), 183–237. doi: https://doi.org/10.1023/b:acap.0000027534.61242.09
- Gluesing-Luerssen, H., Schmale, W. (2006). On Doubly-Cyclic Convolutional Codes. Applicable Algebra in Engineering, Communication and Computing, 17 (2), 151–170. doi: https://doi.org/10.1007/s00200-006-0014-9
- Gomez-Torrecillas, J., Lobillo, F. J., Navarro, G. (2016). A New Perspective of Cyclicity in Convolutional Codes. IEEE Transactions on Information Theory, 62 (5), 2702–2706. doi: https://doi.org/10.1109/tit.2016.2538264
- Rosenthal, J., York, F. V. (1999). BCH convolutional codes. IEEE Transactions on Information Theory, 45 (6), 1833–1844. doi: https://doi.org/10.1109/18.782104
- Rosenthal, J., Smarandache, R. (1999). Maximum Distance Separable Convolutional Codes. Applicable Algebra in Engineering, Communication and Computing, 10 (1), 15–32. doi: https://doi.org/10.1007/s002000050120
- Prihod'ko, S. I., Kuznecov, A. A., Gusev, S. A., Kuzhel', I. E. (2004). Algebraicheskoe postroenie nesistematicheskih svertochnyh kodov. Systemy obrobky informatsiyi, 8 (69), 170–175.
- Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13 (2), 260–269. doi: https://doi.org/10.1109/tit.1967.1054010
- Gluesing-Luerssen, H., Helmke, U., Iglesias Curto, J. (2010). Algebraic decoding for doubly cyclic convolutional codes. Advances in Mathematics of Communications, 4 (1), 83–99. doi: https://doi.org/10.3934/amc.2010.4.83
- Gomez-Torrecillas, J., Lobillo, F. J., Navarro, G. (2017). A Sugiyama-Like Decoding Algorithm for Convolutional Codes. IEEE Transactions on Information Theory, 63 (10), 6216–6226. doi: https://doi.org/10.1109/tit.2017.2731774
- Prihod'ko, S. I., Kuz'menko, D. M. (2008). Method of decoding of algebraic convolution codes. Systemy obrobky informatsiyi, 2 (69), 12–17.
- Ortin, J., Garcia, P., Gutierrez, F., Valdovinos, A. (2009). Two step SOVA-based decoding algorithm for tailbiting codes. IEEE Communications Letters, 13 (7), 510–512. doi: https://doi.org/10.1109/lcomm.2009.090810
- Bushisue, S., Suyama, S., Nagata, S., Miki, N. (2017). Performance Comparison of List Viterbi Algorithm of Tail-Biting Convolutional Code for Future Machine Type Communications. IEICE Transactions on Communications, E100.B (8), 1293–1300. doi: https://doi.org/10.1587/transcom.2016fgp0018
- Han, Y. S., Wu, T.-Y., Chen, P.-N., Varshney, P. K. (2018). A Low-Complexity Maximum-Likelihood Decoder for Tail-Biting Convolutional Codes. IEEE Transactions on Communications, 66 (5), 1859–1870. doi: https://doi.org/10.1109/tcomm.2018.2790935
- Kao, J. W. H., Berber, S. M., Bigdeli, A. (2009). A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion. Advances in Artificial Intelligence, 2009, 1–11. doi: https://doi.org/10.1155/2009/356120
- Rajbhandari, S., Ghassemlooy, Z., Angelova, M. (2012). Adaptive "soft" sliding block decoding of convolutional code using the artificial neural network. Transactions on Emerging Telecommunications Technologies, 23 (7), 672–677. doi: https://doi.org/10.1002/ett.2523
- Azouaoui, A., Chana, I., Belkasmi, M. (2012). Efficient Information Set Decoding Based on Genetic Algorithms. International Journal of Communications, Network and System Sciences, 05 (07), 423–429. doi: https://doi.org/10.4236/ijcns.2012.57052
- Azouaoui, A., Belkasmi, M., Farchane, A. (2012). Efficient Dual Domain Decoding of Linear Block Codes Using Genetic Algorithms. Journal of Electrical and Computer Engineering, 2012, 1–12. doi: https://doi.org/10.1155/2012/503834
- Berkani, A., Azouaoui, A., Belkasmi, M., Aylaj, B. (2017). Improved Decoding of linear Block Codes using compact Genetic Algorithms with larger tournament size. International Journal of Computer Science Issues, 14 (1), 15–24. doi: https://doi.org/10.20943/01201701.1524
- Shtompel, M. (2016). Soft decoding algebraic convolutional codes based on natural computing. Informatsiyno-keruiuchi systemy na zaliznychnomu transporti, 5, 14–18.
- Price, K., Storn, R. M., Lampinen, J. A. (2005). Differential evolution: A practical approach to global optimization. Springer, 539. doi: https://doi.org/10.1007/3-540-31306-0
- Jin, W., Fossorier, M. P. C. (2007). Reliability-Based Soft-Decision Decoding With Multiple Biases. IEEE Transactions on Information Theory, 53 (1), 105–120. doi: https://doi.org/10.1109/tit.2006.887510