Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76151
Title: Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks
Authors: Khan, FN 
Zhong, KP 
Zhou, X
Al-Arashi, WH
Yu, CY 
Lu, C 
Lau, APT 
Issue Date: 2017
Publisher: Optical Society of America
Source: Optics express, 2017, v. 25, no. 15, p. 17767-17776 How to cite?
Journal: Optics express 
Abstract: We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-tonoise 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).
URI: http://hdl.handle.net/10397/76151
ISSN: 1094-4087
EISSN: 1094-4087
DOI: 10.1364/OE.25.017767
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