Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning

• Published in: IEEE photonics technology letters Vol. 29; no. 19; pp. 1667 - 1670
• Main Authors: Wang, Danshi, Zhang, Min, Li, Ze, Li, Jin, Fu, Meixia, Cui, Yue, Chen, Xue
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Back propagation; Back propagation networks; Computer simulation; Convolution; convolution neural network (CNN); Deep learning; eye diagram; Feature extraction; Format; Image processing; Kernel; Machine learning; Modulation; modulation format recognition (MFR); Neural networks; Optical communication; Optical imaging; Optical noise; optical performance monitoring (OPM); optical signal-to-noise rate (OSNR); Recognition; Signal analysis; Signal to noise ratio
• DOI: 10.1109/LPT.2017.2742553
• EISSN: 1941-0174
• ISSN: 1041-1135

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.