Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning
Top Cited Papers
- 21 August 2017
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Photonics Technology Letters
- Vol. 29 (19), 1667-1670
- https://doi.org/10.1109/lpt.2017.2742553
Abstract
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 (10~25 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.Keywords
Funding Information
- National Natural Science Foundation of China (61372119)
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