Support Vector Machine based Decoding Algorithm for BCH Codes

• Autorzy: Sudharsan V. , Yamuna B.
• Języki publikacji: EN

Abstrakty

EN

Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decoding is usually fixed irrespective of the SNR environment in which the transmission takes place, but SVM being a machinelearning algorithm is adaptable to the communication environment. Since the construction of SVM decoder model uses the training data set, application specific decoders can be designed by choosing the training size efficiently. With the soft margin width in SVM being controlled by an equation, which has been formulated as a quadratic programming problem, there are no local minima issues in SVM and is robust to outliers.

Słowa kluczowe

EN

BCH codes; Chase-2 algorithm; coding gain; kernel; multi-class classification; Soft Decision Decoding; support vector machine

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2016
• Tom: nr 2
• Strony: 108--112
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Sudharsan V.

• Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India

autor

Yamuna B.

• Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India

Bibliografia

• [1] J. Kao and S. Berber, “Error control coding based on support vector machine”, in Proc. 1st IAPR Worksh. Cogn. Inform. Process., Santorini, Greece, 2008, pp. 182–187.
• [2] R. Ramanathan, N. Valliappan, S. Pon Mathavan, M. Gayathri, R. Priya, and K. Soman, “Generalised and channel independent SVM based robust decoders for wireless applications”, in Proc. IEEE Int. Conf. Adv. Recent Technol. in Commun. Comput. ARTCom’09, Kottayam, Kerala, Indie, 2009, pp. 756–760.
• [3] R. Bose and D. Ray-Chaudhuri, “On a class of error correcting binary group codes”, Inf. Control., vol. 3, no. 1, pp. 68–79, 1960.
• [4] W. Peterson, “Encoding and error-correction procedures for the Bose-Chaudhuri codes”, IEEE. Trans. Inform. Theory, vol. 6, no. 5, pp. 459–470, 1960.
• [5] D. Gorenstein and N. Zierler, “A class of error-correcting codes in pm symbols”, J. Soc. Ind. Appl. Math., vol. 9, no. 2, pp. 207–214, 1961.
• [6] R. Chien, “Cyclic decoding procedures for Bose-Chaudhuri-Hocquenghem codes”, IEEE Trans. Inform. Theory, vol. 10, no. 4, pp. 357–363, 1964.
• [7] E. Berlekamp, “On decoding binary Bose-Chadhuri-Hocquenghem codes”, IEEE Trans. Inform. Theory, vol. 11, no. 4, pp. 577–579, 1965.
• [8] J. Massey, “Step-by-step decoding of the Bose-Chaudhuri- Hocquenghem codes”, IEEE Trans. Inform. Theory, vol. 11, no. 4, pp. 580–585, 1965.
• [9] G. Forney, “On decoding BCH codes”, IEEE Trans. Inform. Theory, vol. 11, no. 4, pp. 549-557, 1965.
• [10] D. Chase, “A Class of algorithms for decoding block codes with channel measurement information”, IEEE Trans. Inform. Theory, vol. 18, no. 1, pp. 170–182, 1972.
• [11] J. Reeve and K. Amarasinghe, “A parallel Viterbi decoder for block cyclic and convolution codes”, Signal Process., vol. 86, no. 2, pp. 273–278, 2006.
• [12] Y.Wu, “New List Decoding Algorithms for Reed-Solomon and BCH Codes”, IEEE Trans. Inform. Theory, vol. 54, no. 8, pp. 3611–3630, 2008.
• [13] B. Yamuna and T. R. Padmanabhan, “A reliability level list based SDD algorithm for binary cyclic block codes”, Int. J. Comput. Commun. Control, vol. 7, no. 2, pp. 388–395, 2012.
• [14] J. Yuan, L. Wang, Q. He, H. Li, and Y. Wang, “A novel genetic probability decoding (GPD) algorithm for the FEC code in optical communications”, Int. J. Light Elec. Opt., vol. 124, no. 15, pp. 1986–1989, 2013.
• [15] J. Yuan, C. He, W. Gao, J. Lin, and Y. Pang, “A novel hard decision decoding scheme based on genetic algorithm and neural network”, Int. J. Light Electron Opt., vol. 125, no. 14, pp. 3457–3461, 2014.
• [16] A. Azouaoui and M. Belkasmi, “A soft decoding of linear block codes by genetic algorithms”, in Proc. Int. Conf. Intell. Comput. Syst. ICICS’2012, Dubai, United Arab Emirates, 2012.
• [17] X. Zhang, “An efficient interpolation-based chase BCH decoder”, IEEE Trans. Circuits Syst. II: Express Briefs, vol. 60, no. 4, pp. 212–216, 2013.
• [18] H. Torres, M. Jamett, C. Urrea, and J. Kern, “Design of a fault tolerant digital communication system, by means of RBF networks. Comparison simulations with the encoding and decoding algorithms BCH (7,4,1)”, IEEE Latin America Trans., vol. 12, no. 8, pp. 1365–1374, 2014.
• [19] J. Gokulachandran and K. Mohandas, “Comparitive study of two soft computing techniques for the prediction of remaining useful life of cutting tools”, Int. J. Intell. Manuf., vol. 26, no. 2, pp. 255–268, 2013.
• [20] J. Gokulachandran and K. Mohandas, “Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques”, Int. J. Qual. Reliab. Manage., vol. 32, no. 3, pp. 270–290, 2015.
• [21] C. Cortes and V. Vapnik, “Support-vector networks”, Machine Learn., vol. 20, no. 3, pp. 273–297, 1995.
• [22] U. Krebel, “Pairwise classification and Support Vector Machines”, in Advances in Kernel Methods: Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, USA: MIT Press, 1999, pp. 255–270.
• [23] S. Abe, Support Vector Machines for Pattern Classification. Springer, 2005.
• [24] J. Kao, “Methods of artificial intelligence for error control coding and multi-user detection”, Ph.D. Thesis, The University of Auckland, New Zealand, 2010.
• [25] C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines”, IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415–425, 2002.
• [26] C.-C. Chang and C.J. Lin, “LIBSVM: A library for support vector machines”, ACM Trans. Intell. Syst. Technol., vol. 2, no. 2, pp. 27:1–27:27, 2011. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm