Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network

A modulation format identification (MFI) method is proposed for high-speed optical fiber communication systems employing probabilistic shaping (PS) signals in polarization division multiplexing (PDM). The approach utilizes a multi-feature input hybrid neural network (MFHNN) incorporating constellation diagram features and histogram of oriented gradients (HOG) features as dual inputs. These features are trained using a multi-scale convolutional neural network (MS-CNN) and a deep neural network (DNN) to obtain corresponding feature vectors. In the fusion layer, the two feature vectors are merged and classified through fully connected layers, thus constructing an efficient MFI model. The method enhances MFI accuracy by leveraging features of different modulation formats and representations at different neural network levels. To validate the feasibility of the proposed method, signals are collected through the construction of a simulated PDM optical fiber communication system with a fiber length of 80 km and a symbol rate of 50 GBaud. The gathered data is then utilized with the proposed MFI to identify six PS-QAM signals (PS-16QAM with 3 b/symbol and 3.5 b/symbol, PS-64QAM with 4 b/symbol, 4.5 b/symbol, 5 b/symbol, and 5.5 b/symbol) and two uniform shaping (US) QAM signals (US-16QAM with 4 b/symbol and US-64QAM with 6 b/symbol). Simulation results demonstrate that the MFI model constructed by the proposed method achieves an overall identification accuracy of 91.6% for the eight modulation formats when the optical signal-to-noise ratio (OSNR) is within the range of 10 to 30 dB. Compared to traditional MFI methods, our approach significantly improves both MFI accuracy and convergence speed.