Automatic Modulation Classification of Mixed Signals Based on Phase Noise-Insensitive High-Order Cumulant and Distribution Characteristics in Radio-over-Fiber System

To overcome the limitations of existing automatic modulation classification (AMC) methods that mainly target single-signal scenarios in radio-over-fiber (RoF) system, a mixed-signal AMC scheme based on phase noise-insensitive high-order cumulants (PNI-HOC) and distribution characteristics is proposed. The approach enables accurate classification of mixed signals in RoF system. Specifically, a PNI-HOC algorithm is first introduced to mitigate the influence of laser linewidth-induced phase noise. Then, distribution characteristics derived from the signal amplitude histogram are extracted to construct a two-dimensional characteristics space. These characteristics are subsequently fed into decision tree and support vector machine (SVM) classifiers for signal identification. To validate the effectiveness of the scheme, a 10 GBaud RoF system with a 70 km fiber link is implemented. The simulation results show that, compared with the conventional high-order cumulant method, the approach solely based on amplitude histogram distribution characteristics and the scheme based on deep neural networks (DNN) classifier using histogram characteristics, the proposed scheme achieves significantly higher classification accuracy at low optical signal–noise ratios (OSNRs). In particular, when the fiber length is 70 km and the OSNR is ≥16 dB, the classification accuracy of mixed signals is consistently maintained at 100%. Furthermore, the robustness of the proposed method is verified under various system impairments, including laser phase noise, chromatic dispersion and nonlinear effects, amplified spontaneous emission noise, multipath fading, etc., confirming its superior and stable performance.