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Support Vector Machine based Decoding Algorithm for BCH Codes

Sudharsan V., Yamuna B.

Journal of Telecommunications and Information Technology

2016

nr 2

108--112

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.

An Algorithm for BCH Codes Extended with Finite State Automata

Kelarev A.

Fundamenta Informaticae

2008

Vol. 84, nr 1

51-60

This article develops a combinatorial algorithm for a class of codes extending BCH codes and constructed with finite state automata. Our algorithm computes the largest number of errors that the extended codes can correct, and finds a generator for each optimal code in this class of extensions. The question of finding codes with largest possible information rates remains open.