Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11716
Title: Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes
Authors: Khan, FN
Shen, TSR
Zhou, Y
Lau, APT
Lu, C 
Keywords: Artificial neural networks
Asynchronous sampling
Empirical moments
Multi-impairment monitoring
Optical performance monitoring
Issue Date: 2012
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Source: IEEE photonics technology letters, 2012, v. 24, no. 12, 6168795, p. 982-984 How to cite?
Journal: IEEE Photonics Technology Letters 
Abstract: We propose a low-cost technique for simultaneous and independent optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) monitoring in 40/56-Gb/s return-to-zero differential quadrature phase-shift keying (RZ-DQPSK) and 40-Gb/s RZ-DPSK systems, using artificial neural networks (ANN) trained with empirical moments of asynchronously sampled signal amplitudes. The proposed technique employs an extremely simple hardware and digital signal processing to enable multi-impairment monitoring at different data rates and for various modulation formats without necessitating hardware changes. Simulation results demonstrate wide dynamic ranges and good monitoring accuracies.
URI: http://hdl.handle.net/10397/11716
DOI: 10.1109/LPT.2012.2190762
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