Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms

Thomas Shun Rong Shen, Ke Meng, Pak Tao Lau, Zhao Yang Dong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)

Abstract

We propose an optical performance monitoring technique for simultaneous monitoring of optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) using an artificial neural network trained with asynchronous amplitude histograms (AAHs). Simulations are conducted to demonstrate the technique for both 40-Gb/s return-to-zero differential quadrature phase-shift keying (RZ-DQPSK) and 40-Gb/s noneturn-to-zero 16 quadrature amplitude modulation (16-QAM) systems. The OSNR, CD, and PMD monitoring range and root-mean-square (rms) errors are 10-30 and 0.43 dB, 0-400 and 9.82 ps/nm, and 0-10 and 0.92 ps, respectively, for RZ-DQPSK systems. For 16-QAM system, the monitoring range and rms errors are 1030 and 0.2 dB, 0-400 and 9.66 ps/nm, and 0-30 and 0.65 ps for OSNR, CD, and PMD, respectively. As the generation of AAH does not require any clock or timing recovery, the proposed technique can serve as a low-cost option to realize in-service multiparameter monitoring for the next-generation transparent optical networks.
Original languageEnglish
Article number5585710
Pages (from-to)1665-1667
Number of pages3
JournalIEEE Photonics Technology Letters
Volume22
Issue number22
DOIs
Publication statusPublished - 1 Nov 2010

Keywords

  • Amplitude histogram
  • artificial neural network (ANN)
  • asynchronous sampling
  • optical fiber communication
  • optical performance monitoring (OPM)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials

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