The Spectrum Analysis Technology And Application Of Multi-Channel High-Speed Optical Signal Based On Artificial Intelligence

With the rise and development of various emerging Internet services,optical communication systems have playd an irreplaceable role in satisfying the growing number of users and the bandwidth consumed by every user.At present,optical networks are developing in the direction of flexible spectrum grids,complex and diverse modulation formats,and dynamically adjustable transmission rates,which presents new challenges to the traditional optical performance monitoring technologies.Artificial intelligence technology nowadays has already been widely used in optical communication to solve the problem of insufficient flexibility and adaptability of existing optical communication systems.Among which,most of the optical performance monitoring methods based on artificial intelligence are used to analyze and process the time-domain signal waveform after photoelectric detection.In optical communication systems,besides the time-domain signal waveform,the frequency-domain spectral data also contains abundant optical signal information and system performance characteristics.Therefore,spectral analysis is an important research content in processing optical signal and monitoring optical performance.However,in optical communication systems,spectral analysis technology based on artificial intelligence has not received much attention or in-depth research.With the development of flexible and dynamic optical network,spectral analysis technology based on artificial intelligence has important research value and application potential.In view of this,this paper focuses on the spectral analysis technology based on deep learning algorithms and machine learning,and proposes and verifies several solutions.The main innovations of this paper are as follows:Firstly,traditional spectrometers has a high complexity,lack of flexibility in spectral analysis algorithm,and different parameters require different algorithms.To solve these problems,an intelligent spectrum analysis solution based on machine learning is proposed,which makes it possible to analyze the traditional conventional parameters,as well as the damage of the optical signal suffering from the filter cascade and the center frequency offset,and simultaneously estimate the degree of damage.The feasibility of multi-function single wavelength spectral analysis based on support vector machine,which includes optical signal noise ratio,center wavelength,bandwidth,is proved by simulation and experiment.Further more,it can diagnosis the damage of filter cascade and center frequency offset,and estimate the degree of damage.Traditional spectral analysis algorithms require multiple algorithms to analyze only one parameter,which requires a large amount of calculation.For example,when calculating optical signal noise ratio by interpolation,its complexity is O[3(n/2)],and the complexity of analyzing multiple parameters is the sum of the complexity of each algorithm,the result of which will be very large.However,support vector machine can analyze multiple parameters simultaneously.The complexity of support vector machine is only O(nn sv+n sv 3).in which n is the number of sampling points,n sv is the support vector and the number of it is small.What's more,when the support vector machine performs multiple parameter estimation,it has nothing to do with n,which greatly reduces the complexity of the spectrum analysis algorithm and have more flexible and more functional than traditional algorithms.Secondly,traditional algorithm can only analyze a single parameter of a single channel at the same time,and the multi-function single wavelength spectral analysis algorithm based on support vector machine mentioned above can only analyze multiple parameters of a single channel at the same time too.It's obvious that it cannot adapt to the future elastic optical network with a flexible and adjustable spectrum grid,complex and diverse format and a dynamically variable transmission rate.Therefore,based on the above scheme that a single channel spectrum can only be analyzed with multiple parameters simultaneously,a multi-channel elastic spectrum analysis based on the object detection algorithm is proposed,and a simulation analysis platform as well as an experimental analysis platform are bulit to verify the feasibility of the above method.The result of the simulation and experimental show that this scheme can simultaneously monitor the optical signal noise ratio,bandwidth,central wavelength and modulation format of multiple channels by spectrum.In addition,since the object detection algorithm is based on image analysis,it has been verified that converting two-dimensional spectral data into image data can greatly save memory space.In this paper,the storage space is saved by 9 times when the simulation data is converted into image data,and 2.5 times when the experimental data is converted into image data,and it is also an improvement to the waste memory space when analyzing traditional two-dimensional spectra.