Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

131 Citations (Scopus)


We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals’ amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).
Original languageEnglish
Pages (from-to)17767-17776
Number of pages10
JournalOptics Express
Issue number15
Publication statusPublished - 24 Jul 2017

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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