Linear Support Vector Machines for Error Correction in Optical Data Transmission

  • Alex Metaxas
  • Alexei Redyuk
  • Yi Sun
  • Alex Shafarenko
  • Neil Davey
  • Rod Adams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7824)

Abstract

Reduction of bit error rates in optical transmission systems is an important task that is difficult to achieve. As speeds increase, the difficulty in reducing bit error rates also increases. Channels have differing characteristics, which may change over time, and any error correction employed must be capable of operating at extremely high speeds. In this paper, a linear support vector machine is used to classify large-scale data sets of simulated optical transmission data in order to demonstrate their effectiveness at reducing bit error rates and their adaptability to the specifics of each channel. For the classification, LIBLINEAR is used, which is related to the popular LIBSVM classifier. It is found that it is possible to reduce the error rate on a very noisy channel to about 3 bits in a thousand. This is done by a linear separator that can be built in hardware and can operate at the high speed required of an operationally useful decoder.

Keywords

Error correction classification optical communication adaptive signal processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bernstein, G., Rajagopalan, B., Saha, D.: Optical Network Control: Architecture, Protocols, and Standards. Addison Wesley (August 2003)Google Scholar
  2. 2.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  3. 3.
    Collobert, R., Bengio, S.: Links between perceptrons, mlps and svms. In: Brodley, C.E. (ed.) ICML. ACM International Conference Proceeding Series, vol. 69. ACM (2004)Google Scholar
  4. 4.
    Harry, J.R.: Dutton. Understanding optical communications. Prentice Hall PTR (1998)Google Scholar
  5. 5.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)MATHGoogle Scholar
  6. 6.
    Hunt, S., Sun, Y., Shafarenko, A., Adams, R., Davey, N., Slater, B., Bhamber, R., Boscolo, S., Turitsyn, S.K.: Correcting Errors in Optical Data Transmission Using Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part II. LNCS, vol. 6353, pp. 448–457. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Hunt, S., Sun, Y., Shafarenko, A., Adams, R., Davey, N., Slater, B., Bhamber, R., Boscolo, S., Turitsyn, S.K.: Adaptive Electrical Signal Post-processing with Varying Representations in Optical Communication Systems. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds.) EANN 2009. CCIS, vol. 43, pp. 235–245. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Maliuk, D., Stratigopoulos, H.-G., Makris, Y.: An analog vlsi multilayer perceptron and its application towards built-in self-test in analog circuits. In: Proceedings of the 2010 IEEE 16th International On-Line Testing Symposium, IOLTS 2010, pp. 71–76. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  9. 9.
    Nielsen, J.: Nielsen’s law of internet bandwidth (1998), http://www.useit.com/alertbox/980405.html
  10. 10.
    Sun, Y., Shafarenko, A., Adams, R., Davey, N., Slater, B., Bhamber, R., Boscolo, S., Turitsyn, S.K.: Adaptive electrical signal Post-Processing in optical communication systems (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alex Metaxas
    • 1
  • Alexei Redyuk
    • 2
  • Yi Sun
    • 1
  • Alex Shafarenko
    • 1
  • Neil Davey
    • 1
  • Rod Adams
    • 1
  1. 1.Biological and Neural Computation Research GroupSchool of Computer Science University of HertfordshireHatfieldUK
  2. 2.Novosibirsk State UniversityRussia

Personalised recommendations