A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

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).

Deep Transfer Clustering of Radio Signals

“…However, it is real a challenge to cluster radio signals for modulation recognition since the signal waves of the same modulation type could be quite different, while those of different modulation types may be close to each other, since the difference of signal waves could be largely determined by the transmitted information. To the best of our knowledge, there are few studies on the modulation clustering of radio signals, which is used to analyze the importance of various features in modulation recognition [1], and to reconstruct the cluster center vector of the constellation diagram [2]- [5].…”