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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.

Spectral Analysis of Boosted Space-Time Diversity Scheme

Krasicki M.

Advances in Electronics and Telecommunications

2010

Vol. 1, no. 2

17--21

In this paper the asymptotic performance of a new intuitive space-time diversity scheme is analyzed. So called boosted scheme is compatible with today’s WLAN specifications with regard to convolutional coding and bit labelling, and minimizes the number of decoding iterations, required to obtain a reasonable Bit Error Rate. Good properties of the proposed scheme are proved by high asymptotic coding gain and advantageous distance spectrum. A simulation experiment is run to investigate the system performance in terms of poor channel state. The boosted scheme is compared with its ancestor – Bit-Interleaved Space-Time Coded Modulation with Iterative Decoding (BI-STCM-ID).