Joint Symbol Rate-Modulation Format Identification and OSNR Estimation using Random Forest Based Ensemble Learning for Intermediate Nodes

In this paper, a novel joint symbol rate-modulation format identification (SR-MFI) and optical signal-to-noise ratio (OSNR) estimation scheme using the low-bandwidth coherent detecting and random forest (RF)-based ensemble learning is proposed for intermediate nodes in the flexible dense wavelength division multiplexing (F-DWDM) networks. By leveraging low-bandwidth coherent detecting with small bulk wavelength scanning, no chromatic dispersion compensation and low-complexity RF, the proposed scheme could serve as a reduced-complexity and cost-effective option to realize joint SR-MFI and OSNR estimation at intermediate nodes in F-DWDM networks. To verify the feasibility of the proposed scheme, the comprehensive simulations of 8/16 GBaud polarization division multiplexing (PDM)-4/16/32/64 quadrature amplitude modulation (QAM) systems are conducted. The simulation results show that the identification accuracy of SR-MFI reaches 100% and the mean absolute error of OSNR estimation is within 1 dB. Moreover, the proposed monitoring scheme is verified by 8/16 GBaud PDM-4/16/32QAM coherent transmission experiments.