# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 5 Conclusions  
We proposed a novel real-time ROADM failure detection and prediction system that utilizes the tapped raw optical signal from a digital coherent optical receiver. To achieve real-time monitoring, we implemented container-based applications within the Cassini WhiteBox. These container applications include tapping digital signal data immediately after the ADCs in receiver LSI, storing the acquired data from Cassini, and providing it for machine learning to detect an optical filter shift. As a result, we successfully confirmed the detection of the optical filter shift with high accuracy (nearly 100%) by using DP-16 QAM constellations data before demodulation for machine learning at a 51 GHz shift. Moreover, we proposed a novel data augmentation technique of the digital signal data captured from a digital coherent LSI to reduce the amount of data needed for machine learning. We successfully cloned the constellation data by using a DC-GAN trained on actual constellation data. We confirmed enhanced sensitivity in optical filter shift detection (26 GHz) with a reduced amount of actual data (68% reduction) for machine learning. Additionally, by analyzing changes in BERs before SD-FEC decoders, the optical filter shift could be detected before the degradation of the BERs, demonstrating the effectiveness of our proposed approach for fault prediction. The estimation of the actual timeframe from the detection of optical filter shift to the occurrence of BER degradation will be the focus of future work.  
As the demand for diverse communication services continues to expand and communication technologies advance at an ever-increasing pace, we remain committed to improving the accuracy of real-time sensing and further developing our proposed CAT platform to reduce OPEX in network operation and ensure a sustainable future of such a complex network by providing an autonomous fault diagnosis system. By enhancing the performance and reliability of these technologies, we aim to create an environment where users can access communication services with greater comfort, efficiency, and convenience. Our goal is to contribute to a future where communication infrastructure supports seamless connectivity, meeting the needs of a rapidly evolving digital society.  
Conceptualization, A.H., Y.N. and S.N.; methodology, A.H., Y.N., S.N. and T.N.; software, Y.N., S.N. and T.N.; validation, S.N. and T.N.; formal analysis, T.N.; investigation, T.N.; data curation, T.N.; writing--original draft preparation, T.N.; writing--review and editing, A.H.; visualization, T.N. and A.H.; supervision, A.H.; project administration, A.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study is partially supported by the Support Center for Advanced Telecommunications Technology Research (SCAT) in Japan.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.