Correcting errors in optical data transmission using neural networks

  • Authors:

  • Stephen Hunt;Yi Sun;Alex Shafarenko;Rod Adams;Neil Davey;Brendan Slater;Ranjeet Bhamber;Sonia Boscolo;Sergei K. Turitsyn

  • Affiliations:

  • Biological and Neural Computation Research Group, School of Computer Science, University of Hertfordshire, Hatfield, Herts, UK;Biological and Neural Computation Research Group, School of Computer Science, University of Hertfordshire, Hatfield, Herts, UK;Biological and Neural Computation Research Group, School of Computer Science, University of Hertfordshire, Hatfield, Herts, UK;Biological and Neural Computation Research Group, School of Computer Science, University of Hertfordshire, Hatfield, Herts, UK;Biological and Neural Computation Research Group, School of Computer Science, University of Hertfordshire, Hatfield, Herts, UK;Photonics Research Group, School of Engineering and Applied Science, Aston University, Birmingham, UK;Photonics Research Group, School of Engineering and Applied Science, Aston University, Birmingham, UK;Photonics Research Group, School of Engineering and Applied Science, Aston University, Birmingham, UK;Photonics Research Group, School of Engineering and Applied Science, Aston University, Birmingham, UK

  • Venue:
  • ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
  • Year:
  • 2010

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Abstract

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.