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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 11 — May. 21, 2012
  • pp: 12422–12431

Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks

Faisal Nadeem Khan, Yudi Zhou, Alan Pak Tao Lau, and Chao Lu  »View Author Affiliations

Optics Express, Vol. 20, Issue 11, pp. 12422-12431 (2012)

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We propose a simple and cost-effective technique for modulation format identification (MFI) in next-generation heterogeneous fiber-optic networks using an artificial neural network (ANN) trained with the features extracted from the asynchronous amplitude histograms (AAHs). Results of numerical simulations conducted for six different widely-used modulation formats at various data rates demonstrate that the proposed technique can effectively classify all these modulation formats with an overall estimation accuracy of 99.6% and also in the presence of various link impairments. The proposed technique employs extremely simple hardware and digital signal processing (DSP) to enable MFI and can also be applied for the identification of other modulation formats at different data rates without necessitating hardware changes.

© 2012 OSA

OCIS Codes
(060.1660) Fiber optics and optical communications : Coherent communications
(060.2330) Fiber optics and optical communications : Fiber optics communications
(060.2360) Fiber optics and optical communications : Fiber optics links and subsystems
(060.4510) Fiber optics and optical communications : Optical communications
(060.5060) Fiber optics and optical communications : Phase modulation

ToC Category:
Fiber Optics and Optical Communications

Original Manuscript: April 23, 2012
Revised Manuscript: May 11, 2012
Manuscript Accepted: May 11, 2012
Published: May 16, 2012

Faisal Nadeem Khan, Yudi Zhou, Alan Pak Tao Lau, and Chao Lu, "Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks," Opt. Express 20, 12422-12431 (2012)

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