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

Faisal Nadeem Khan, Yudi Zhou, Pak Tao Lau, Chao Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

103 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)12422-12431
Number of pages10
JournalOptics Express
Volume20
Issue number11
DOIs
Publication statusPublished - 21 May 2012

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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