Abstract
In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).
Highlights
In the past 10 years, considerable attention has been given to optical network technology.Traditional wavelength division multiplexing (WDM) optical networks with fixed wavelength and a single modulation format are facing challenges
We propose a joint algorithm for modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation based on DBSCAN in this work
Photonics Inc (Norwood, MA, USA). and the DBSCAN-based joint algorithm is used at the receiver end to identify the modulation formats (MFs) and estimate OSNR
Summary
In the past 10 years, considerable attention has been given to optical network technology. Modulation format identification (MFI) technique is indispensable for re-configurable digital coherent receivers used in EONs. It is imperative to have appropriate monitoring mechanisms across the networks to acquire precise and real-time information about the quality of transmission links and health of optical signals [3,4]. Khan et al propose a method combining optical signal-to-noise ratio (OSNR) monitoring and MFI based on signals’ amplitude histograms with the use of deep neural networks [29]. By fitting the proportion of core points in the sample set, the DBSCAN-based method can realize OSNR estimation for five MFs mentioned above with low mean absolute error even when the testing data is not trained. In Appendix A, we present the basic notion, pseudocode, and framework of DBSCAN algorithm
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