Authors:
Weam M. Binjumah
1
;
Alexey Redyuk
2
;
Rod Adams
3
;
Neil Davey
3
and
Yi Sun
3
Affiliations:
1
University of Hertfordshire and Taibah University, United Kingdom
;
2
Institute of Computational Technologies SB RAS, Russian Federation
;
3
University of Hertfordshire, United Kingdom
Keyword(s):
Support Vector Machine (SVM), Machine Learning, Optical Signals, Coherent Optical Communications, Error Correction, Wavelet Transform.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
Reducing bit error rate and improving performance of modern coherent optical communication system is a significant issue. As the distance travelled by the information signal increases, bit error rate will degrade. Support Vector Machines are the most up to date machine learning method for error correction in optical transmission systems. Wavelet transform has been a popular method to signals processing. In this study, the properties of most used Haar and Daubechies wavelets are implemented for signals correction. Our results show that the bit error rate can be improved by using classification based on wavelet transforms (WT) and support vector machine (SVM).