Support vector machine based decoding algorithm for BCH codes

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. Keywords—BCH codes, Chase-2 algorithm, coding gain, kernel, multi-class classification, Soft Decision Decoding, Support Vector Machine.

[1]  Yingquan Wu,et al.  New List Decoding Algorithms for Reed–Solomon and BCH Codes , 2007, IEEE Transactions on Information Theory.

[2]  G. David Forney,et al.  On decoding BCH codes , 1965, IEEE Trans. Inf. Theory.

[3]  B. Yamuna,et al.  A Reliability Level List based SDD Algorithm for Binary Cyclic Block Codes , 2012, Int. J. Comput. Commun. Control.

[4]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[5]  Hao Li,et al.  A novel genetic probability decoding (GPD) algorithm for the FEC code in optical communications , 2013 .

[6]  David Chase,et al.  Class of algorithms for decoding block codes with channel measurement information , 1972, IEEE Trans. Inf. Theory.

[7]  Dwijendra K. Ray-Chaudhuri,et al.  Binary mixture flow with free energy lattice Boltzmann methods , 2022, arXiv.org.

[8]  K. Mohandas,et al.  Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques , 2015 .

[10]  James L. Massey,et al.  Step-by-step decoding of the Bose-Chaudhuri- Hocquenghem codes , 1965, IEEE Trans. Inf. Theory.

[11]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[12]  Elwyn R. Berlekamp,et al.  On decoding binary Bose-Chadhuri- Hocquenghem codes , 1965, IEEE Trans. Inf. Theory.

[13]  Robert T. Chien,et al.  Cyclic decoding procedures for Bose- Chaudhuri-Hocquenghem codes , 1964, IEEE Trans. Inf. Theory.

[14]  Ahmed Azouaoui,et al.  A Soft Decoding of Linear Block Codes by Genetic Algorithms , .

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  John Kern Molina,et al.  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) , 2014 .

[17]  Yu Pang,et al.  A novel hard decision decoding scheme based on genetic algorithm and neural network , 2014 .

[18]  W. W. Peterson,et al.  Encoding and error-correction procedures for the Bose-Chaudhuri codes , 1960, IRE Trans. Inf. Theory.

[19]  Stevan M. Berber,et al.  ERROR CONTROL CODING BASED ON SUPPORT VECTOR MACHINE , 2008 .

[20]  Shigeo Abe Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[21]  N. Zierler,et al.  A Class of Error-Correcting Codes in $p^m $ Symbols , 1961 .

[22]  K. P. Soman,et al.  Generalised and Channel Independent SVM Based Robust Decoders for Wireless Applications , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[23]  Jeffrey S. Reeve,et al.  A parallel Viterbi decoder for block cyclic and convolution codes , 2006, Signal Process..

[24]  Johnny Wei-Hsun Kao Methods of artificial intelligence for error control coding and multi-user detection , 2010 .

[25]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[26]  K. Mohandas,et al.  Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools , 2015, J. Intell. Manuf..

[27]  K. Bala Krishna,et al.  An Efficient Interpolation-Based Chase BCH Decoder , 2014 .