# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 1 Introduction  
In recent years, with the advent of digitalization and various online services such as subscription video-on-demand services (SVOD), the amount of data traffic on the network has been increasing. In addition to that, in the Beyond-5G, 6G era, virtualized network slices on top of the physical network will achieve various services to meet each requirement in the next-generation networks. Moreover, various virtual network operators, including mobile virtual network operators (MVNOs), will provide novel services and applications on such network slices. As a natural consequence of such developments, network complexity and diversification will be expected to explode, including network slicing and other virtualization network technologies [1]. On the other hand, the continued adoption of advanced technologies such as software-defined networking (SDN), and network functions virtualization (NFV) will necessitate future networks to achieve even greater levels offlexibility and programmability in both management and service delivery. These networks will not only expand significantly in size and complexity but will also need to dynamically adapt to diverse and evolving requirements. A vast array of network slices, each tailored to specific needs, will operate on a layered and interconnected infrastructure, managed by various network service and content providers. Consequently, the scale, heterogeneity, and intricacy of these systems are expected to grow to a point were traditional, human-driven administration will no longer be sufficient. Given this background, autonomous network diagnosis [2; 3] and proactive maintenance [4] by fault prediction are attracting attention for the purpose of economizing on manpower and reducing the cost of network maintenance and operations. Various drivers are pushing toward the automation of networks. On the other hand, to reduce equipment costs, there is a strong trend toward network disaggregation [5; 6; 7]. As shown in Figure 1, disaggregation refers to the openness of network equipment. Traditionally, as shown in this figure under the term \"aggregated network\", all network components are typically supplied by a single vendor. In contrast, the disaggregated approach allows for the selection of individual components from a variety of vendors. This shift encourages price competition among vendors, leading to potential reductions in CAPital EXpenditures (CAPEX) over time. Such devices are called WhiteBox, since we can see the inside of the box. From an OPERational EXpenditure (OPEX) perspective, the aggregated approach offers a significant advantage in terms of clear accountability for network failures, such as transponder malfunctions. In an aggregated setup, the vendor responsible for providing the network components assumes full responsibility for resolving failures and restoring normal operations. In contrast, the disaggregated approach involves components sourced from multiple vendors, which can introduce ambiguity when diagnosing failures that occur between components, such as an issue involving Component A from Vendor A and Component B from Vendor B. In such cases, identifying the root cause of the failure and determining accountability can become a complex and time-consuming task. To address this challenge, autonomous network diagnosis plays a crucial role. By enabling precise fault detections and analysis, it minimizes the disadvantages associated with the disaggregated approach, ensuring efficient troubleshooting and reducing the operational burden. In TIP [6], various WhiteBox packet transponders have been designed  
Figure 1: Optical network disaggregation. and implemented, such as Cassini and Voyager. To promote open innovations based on WhiteBox, a multi-vendor inter-operability demonstration is reported [8]. Moreover, we can implement various container-based applications such as network monitoring functions in WhiteBox.