# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 3 AI-Based Modulation Format Recognition  
### Format Recognition System Based on Inception-ResNet-v2 Convolutional Neural Network Modeling  
Service signals from different customer sides in the optical network may be carried through various modulation formats, requiring fast, flexible, and intelligent format recognition within the network. The recognition of three modulation formats is realized based on the Inception-ResNet-v2 convolutional neural network model in this study, and the core idea is to integrate the Inception module with the ResNet module, where the Inception module captures multi-scale features by parallelizing multiple convolutional kernels of different sizes, while the ResNet module addresses the issues of gradient vanishing and exploding in deep networks through residual connections, which facilitates the network's learning of complex mapping relationships, thereby enhancing the model performance and stability, and facilitating better training of deep models [10; 11]. Since the Inception-ResNet-v2 model can extract features from different angles and scales, it has a stronger capability to recognize subtle differences in optical communication signals. This study uses eye diagrams of signals to identify different modulation formats. When recognizing eye diagrams of different modulation formats, the Inception-ResNet-v2 model can capture unique features specific to each modulation format, thereby improving recognition accuracy. Figure 7: Eye diagrams of the four-channel signals for the three types of signals at the launch power of 6 dBm and transmission distance of 1500 km: (**a**) first channel; (**b**) second channel; (**c**) third channel; (**d**) last channel. As shown in Figure 8, the signals emitted by the 100 Gbps Apol-CRZ-FSK, CRZ-FSK, and DQPSK signal transmitters studied in the previous text are transmitted through a single-mode fiber (SMF) with lengths ranging from 30 km to 180 km. Subsequently, post-dispersion compensation is performed using DCF of corresponding lengths. A set of OSNR components is used to load the defined noise floor into the signal, with the OSNR varying from 6 dB to 30 dB in steps of two. The eye diagrams of different modulation formats are captured on the receiving side of the system through the filter, and the processed data yield a 299 \(\times\) 299 \(\times\) 3 dataset for format recognition. Initially, the eye diagram data are input into a Stem layer, followed by passing through Inception-ResNet-A, Inception-ResNet-B, and Inception-ResNet-C sequentially, with a Reduction module inserted between each two layers. Then, the data are compressed and dimension-reduced through an Average Pooling layer to optimize and decrease network complexity. Finally, the data go through a Dropout layer and a Fully Connected layer, concluding with the output via a SoftMax function. The Stem module in Figure 8 utilizes an adaptive deformable spatial transformer to reposition, scale, and rotate the input feature maps, endowing the network with robustness to different transformations of the input images, and automatically aligning features from various angles, sizes, and positions. Moreover, the Stem module adopts a branch-and-connect approach, merging the spatially transformed feature maps with the original ones, preserving the low-level features from the original feature maps while enhancing the ability to distinguish adversarial samples using the transformed feature maps. The \(1\times 1\) convolution operation primarily serves to reduce data dimensions and introduce nonlinearity, thereby improving the network's expressive power. The Equation (4) for \(1\times 1\) convolution is as follows [21]:  
\[Conv_{1\times 1}(X)_{i,j,k}=\sum_{c=1}^{C}W_{1,c,k}X_{i,j,c}+b_{k} \tag{4}\]  
where \(W_{1,c,k}\) represents the weight between the channel c and the channel \(k\) of the \(1\times 1\) convolution kernel, \(b_{k}\) denotes the bias of the channel \(k\), and \((X)_{i,j,k}\) signifies the value of the output feature map at the row \(i\), column \(j\), and channel \(k\).