Modulation Format Identification in Coherent Receivers Using Deep Machine Learning

Faisal Nadeem Khan, Kangping Zhong, Waled Hussein Al-Arashi, Changyuan Yu, Chao Lu, Pak Tao Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

134 Citations (Scopus)


We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals' amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Experimental results for three commonly-used modulation formats demonstrate MFI with an accuracy of 100% over a wide optical signal-to-noise ratio (OSNR) range. The effects of fiber nonlinearity on the performance of MFI technique are also investigated. The proposed technique is non-data-aided (NDA) and avoids any additional hardware on top of standard digital coherent receiver. Therefore, it is ideal for simple and cost-effective MFI in future heterogeneous optical networks.
Original languageEnglish
Article number7482803
Pages (from-to)1886-1889
Number of pages4
JournalIEEE Photonics Technology Letters
Issue number17
Publication statusPublished - 1 Sept 2016


  • coherent detection
  • deep machine learning
  • Modulation format identification

ASJC Scopus subject areas

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


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