Optical Spectrum Identification And In?band OSNR Monitoring Based On Machine Learning Techniques

At present,optical fiber communication systems are moving toward ultra-high speed,large capacity,ultra-long distance and dynamic reconfigurability.In order to realize the intelligent management of optical networks under such development trend,it is necessary to identify optical signals of various speeds and modulation formats,and monitor the optical signal-to-noise ratio(OSNR)of their key quality indicators.This paper focuses on signal learning recognition technology based on machine learning and in-band OSNR monitoring technology.The signal spectrum is measured by a new ultrahigh resolution spectrometer based on stimulated Brillouin scattering.This paper first introduces the research background and development status of related technologies,and then proposes a signal spectrum recognition method based on principal component analysis and support vector machine,which realizes the accurate identification of nine common signals,with dispersion,polarization mode dispersion,and has the advantages of nonlinear effects and large tolerances of cascaded filtering effects.Based on this,a new in-band OSNR monitoring method based on reference spectroscopy is proposed,which solves the problem that the same method can't realize the optical signal OSNR monitoring,modulation distortion and link condition change in dynamic optical networks,and has the advantages of high precision and great tolerance to signal damage.