# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 1 Introduction
Advancements in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), are already driving progress in this direction, laying the foundation for autonomous network operation and management. In the field of optical networking, digital coherent optical transmission systems have been put into practical use, and it has become common practice to digitize optical signals for signal processing. Therefore, it can be said that the groundwork has been laid for the application of machine learning using neural networks to optical signals as well [9; 10]. For autonomous network diagnosis, we proposed a (Collecting, Analyzing, and Testing) CAT platform for autonomous network diagnosis, as shown in Figure 2. The CAT platform autonomously performs network diagnosis by executing iterative diagnostic cycles without human intervention. In each cycle, it collects various types of information, such as logs, alerts, and sensor data, analyzes the collected data to identify potential issues, conducts further tests based on the analysis results, and then gathers additional information. This process is repeated until the root cause of the issue is identified, ensuring efficient and accurate network diagnosis [11; 12; 13]. When human operators face some ambiguous situation (question marks in the figure), they will arrange additional tests to further dig into toward final cause. The analyzing (yellow arrow) and testing blocks (blue arrow) generates additional tests to diagnose as the next step as shown in the figure. When they got additional results, but could not reach the final cause, then they will generate further additional tests to clarify cause. This cyclic testing continues until it gets final cause of corresponding faults (star marks in the figure). Conventional network maintenance tasks require multiple types/times of test measurements to find the root cause of a failure. Such network diagnostic algorithms can be realized in the CAT platform, which is implemented in the open-source software StackStorm [14]. CAT algorithms in the StackStorm iterate through various network tests to find the final cause of failure by automatically performing these diagnostic steps. The algorithm is written in Yet Another Markup Language (YAML). The CAT platform is expected to realize autonomous network diagnosis and failure prediction. Network failures resulting from various causes including network equipment failures, incorrect operation, and network congestions, among others, will lead to disruptions in network services. Therefore, extensive studies have been carried out to realize prompt  
Figure 2: Functions of the CAT platform. network diagnosis and network failure predictions. Link failure detection and prediction in mobile ad hoc network are summarized in [15]. Machine learning techniques such as long short-term memory (LSTM) have been used for root cause analysis and prediction of microwave networks [16]. Decision trees and support vector machine (SVM) are used for LAN failure predictions [17]. As for optical networks, various techniques including machine learning have been extensively investigated [18]. In optical backbone and metro area networks, reconfigurable optical add/drop multiplexers (ROADMs) are employed to support cost-effective optical routing function. Conventional ROADM nodes have many optical switches, which have optical steering and filtering functions [19]. An optical filter shift in ROADM nodes in optical network impacts the Quality of Transmission (QoT). In this article, we introduce a novel real-time data acquisition method from digital coherent optical receiver and approaches for optical filter shift detection. In addition, we introduce an overview of a GAN and an explanation of a DC-GAN (Deep Convolutional-GAN) [20] for data augmentation in Section 2. Then, we describe the experimental setup for performance evaluation of our novel approaches in Section 3. In Section 4, we discuss the evaluation results. Finally, we provide the conclusion in Section 5.