Low-Complexity and Nonlinearity-Tolerant Modulation Format Identification Using Random Forest

A novel low-complexity and nonlinearity-tolerant modulation format identification (MFI) using random forest (RF) is proposed for the flexible coherent receivers (FCRs). The RF-based MFI takes the advantage of distinct features from the amplitude histograms (AHs) and the swarm intelligence of RF to significantly reduce the computational complexity compared with the deep learning-based method. Both numerical simulation and experiments are conducted. The simulation results of polarization multiplexed (PM) 4/8/16/32/64 quadrature amplitude modulation (QAM) wavelength division multiplexing (WDM) systems demonstrate its feasibility and identification performance. In the presence of nonlinear effects, with lower complexity than three other machine learning algorithms (k-nearest neighbors, support vector machine, and deep neural networks), the RF-based MFI can obtain 100% accuracy at the OSNR values greater than or equal to the respective soft decision forward error correction (SD-FEC) threshold. The identification accuracy versus total launch power is experimentally investigated in PM-4/16/32QAM WDM coherent systems. In the case of obvious nonlinear impairments, 100% accuracy can also be achieved above the OSNR corresponding to the SD-FEC threshold. The superiority of the proposed MFI method makes it highly desirable for applications in modulation format-adaptive FCRs.