This paper proposes and demonstrates an Optical Signal Noise Ratio(OSNR) monitoring scheme using the signal power nonlinear transformation and Deep Neural Networks(DNN). The features of signal after 2
th
4
th
and 8
th
transformation and corresponding Fast Fourier Transformation(FFT) depend on the OSNR of signal. By utilizing the DNN to extract those OSNR depended specific features
the OSNR value can be estimated. Simulation results for 28 Gbaud Polarization Division Multiplexing(PDM)-Quadrature Phase Shift Keying(QPSK)
PDM-8 Phase Shift Keying(PSK)
PDM-8 Quadrature Amplitude Modulation(QAM) and PDM-16 QAM signals show that the OSNR monitoring with mean estimation standard errors of 0.10
0.09
0.33 and 0.46 dB in back-to-back case and 0.43
0.34
0.66 and 0.79 dB in 2 000
1 040
1 040 and 800 km single mode fiber transmission case with input optical power of 4
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Related Institution
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications
Shanghai Collaborative Innovation Center of Low-Earth-Orbit Satellite Communication Technology
Shanghai Engineering Research Center of Low-Earth-Orbit Satellite Communication and Applications
Peng Cheng Laboratory
The Ministry of Education Key Laboratory of Electromagnetic Wave Information Science, Department of Communication Science and Engineering, Fudan University