# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
### Prior Works for MFI  
Telecommunications operators in various countries generally adopt different equipment of various speed levels from different manufacturers to construct the backbone network, metro network, and access network of computing optical networks, and carry service signals for different customers through different optical modulation technologies. Computing optical networks are evolving into more complex, dynamic, flexible, and intelligent systems. To meet the demand for signal transmission and various computing capabilities in the era of intelligent connection of everything, computing optical networks also need to perceive business and computing information; intelligently identify different modulation formats carrying signals from different customers; and dynamically adjust data transmission wavelength, signal modulation format, signal power, and other parameters based on link conditions and user service requirements. Therefore, ascertaining how to enhance the transmission performance of signals through advanced modulation formats and quickly distinguish signal formats in computing infrastructure to achieve intelligent modulation format identification (MFI) have become research hotspots.  
Classical MFI algorithms are mainly divided into either likelihood ratio decision theory-based or feature extraction-based. Methods based on likelihood ratio decision theory require consideration of a large amount of prior knowledge such as the mean, variance, and SNR of communication signals [10]. This approach is computationally complex and lacks robustness. Methods based on feature extraction require less prior knowledge, are simpler to analyze, and have higher efficiency [11]. These methods distinguish between different modulation formats by extracting characteristic parameters from the signals [12; 13] and are greatly influenced by the selection of features, and different features can lead to significant differences in recognition results. Therefore, determining how to select features is crucial to the quality of recognition results. Since feature extraction takes a certain amount of time, it is not possible to achieve real-time processing in practical applications.  
In recent years, neural network technology has been widely used in real-time data processing, feature extraction, and image recognition. Research has shown that the recognition of 11 modulation types based on a time-frequency feature fusion strategy using a Squeeze-and-Excitation Network (SENet) achieved a maximum recognition accuracy of 92.5%, and the average recognition accuracy reached 90.87% above 0 dB, outperforming other deep learning algorithms [14]. The use of a joint DNN to recognize the amplitude statistical histogram of radio frequency signals achieved a recognition accuracy of 96.05%, significantly improving the recognition accuracy of radio frequency signals under low SNR conditions [15]. A scheme based on convolutional neural networks and equal weight multitask learning extracts shared features of the OSNR and modulation formats (10 Gbps QPSK, QAM8, QAM16, QAM64) from constellation diagrams and uses them for multi-labelclassification. Simulation results show that the accuracy of the MFI and OSNR can stably and continuously reach 100% [16].  
Neural network technology utilizes deep neural networks with multiple nonlinear layers to learn and extract feature information from signals, without relying on manual experience to extract signal features [17]. MFI methods based on neural network technology have the advantages of requiring less prior knowledge, strong feature extraction capabilities, and high recognition accuracy, which have received widespread attention and research from scientific researchers [18; 19; 20].