Photonic machine learning implementation for signal recovery in optical communications

Apostolos Argyris, Julián Bueno, Ingo Fischer

Research output: Contribution to journalArticlepeer-review

135 Citations (Scopus)
68 Downloads (Pure)


Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when these signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap.

Original languageEnglish
Article number8487
Number of pages13
JournalScientific Reports
Issue number1
Publication statusPublished - 31 May 2018


  • machine learning techniques
  • nonlinear transient responses
  • information processing
  • optical communication signals
  • distortion


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