期刊
IEEE PHOTONICS TECHNOLOGY LETTERS
卷 29, 期 19, 页码 1667-1670出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LPT.2017.2742553
关键词
Machine learning; deep learning; convolution neural network (CNN); eye diagram; optical performance monitoring (OPM); optical signal-to-noise rate (OSNR); modulation format recognition (MFR)
资金
- NSFC [61372119]
An intelligent eye-diagram analyzer is proposed to implement both modulation format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using a convolution neural network (CNN)-based deep learning technique. With the ability of feature extraction and self-learning, CNN can process eye diagram in its raw form (pixel values of an image) from the perspective of image processing, without knowing other eye-diagram parameters or original bit information. The eye diagram images of four commonly-used modulation formats over a wide OSNR range (10 similar to 25 dB) are obtained from an eye-diagram generation module in oscilloscope combined with the simulation system. Compared with four other machine learning algorithms (decision tress, k-nearest neighbors, back-propagation artificial neural network, and support vector machine), CNN obtains the higher accuracies. The accuracies of OSNR estimation and MFR both attain 100%. The proposed technique has the potential to be embedded in the test instrument to perform intelligent signal analysis or applied for optical performance monitoring.
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