Open AccessJoint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks
Author(s) -
Faisal Nadeem Khan,
Kangping Zhong,
Xian Zhou,
Waled Hussein Al-Arashi,
Yu Chen,
Chao Lu,
Alan Pak Tao Lau
Publication year - 2017
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.25.017767
Subject(s) - quadrature amplitude modulation , phase shift keying , computer science , qam , modulation (music) , artificial neural network , optics , electronic engineering , amplitude and phase shift keying , algorithm , physics , bit error rate , artificial intelligence , decoding methods , engineering , acoustics
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).