# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 3 Optical Filter Shift Detection and Data Augmentation  
### Experimental Setup  
Figure 7 shows the experimental setup for optical filter shift detection and the White-Box apparatus used in this experiment. A CFP2-ACO transceiver installed in the Cassini WhiteBox is used as the optical source. The transmitter wavelength and corresponding linewidth of the CFP2-ACO transceiver are 1550 nm and 300 kHz, respectively. Thus, its coherent length can be estimated as approximately 300 m [28]. The optical signal is launched into a fiber link that consists of a G.652 standard Single-Mode Fiber (SMF) and an optical variable ATTenuator (ATT). The transmitter generates a Dual-Polarization (DP) 16 Quadrature Amplitude Modulation (16 QAM) optical signal of 25 GBd. The optical signal at the transmitter with 1 dBm of output power is launched into a flat-top optical filter with an FWHM bandwidth of 121 GHz through the ATT of 5 dB. The filter edge rolling-off slope is 55 dB/nm. The output from the optical filter was measured by OSA. The resolution of the OSA is 0.06 nm. For real-time data acquisition, as shown in Figure 8a, we implemented Docker container applications in the Cassini WhiteBox packet transponder [25] to acquire/store constellation data and to test trained CNN models. In the demodulation module in the LSI, we implemented two types of Forward Error Correction (FEC) codes. One is a Soft-Decision (SD) FEC with 13% redundancy and another one is Hard-Decision (HD) FEC with 7% redundancy. Figure 8b shows a heatmap obtained by the Docker container applications. This is a contour map where brighter color means high population. These two codes are concatenated to ensure reliable transmission, as shown in Figure 9. The Docker container applications can also monitor the Bit Error Rate (BER) before SD-FEC, HD-FEC and after these decoders at the tapping points, as shown in Figure 9.  
The Cassini WhiteBox is a versatile platform capable of hosting various container-based applications tailored for different purposes, such as data collection and information monitoring, as outlined in [25]. The tapped digital waveform data are temporarily stored in a database container application implemented in Cassini. For machine learning purposes, we accumulated the sampled digital data, consisting of I/Q  
Figure 7: (**a**) Experimental setup for optical filter shift detection; (**b**) WhiteBox packet transponder Cassini. timeframe. From these data, we generated 2D histograms with a resolution of 64 \(\times\) 64 for each polarization (X and Y), as shown Figure 8b. This histogram represents a heat map, with the green area in the center showing higher frequencies. The captured data were sent to a Redis database container and stored in the container. The TRDS data were retrieved from the database and used to train CNN models in GPU-installed workstation server. Figure 10 illustrates the software stack of our WhiteBox transponder system. We deployed network diagnosis containers in addition to the Kubernetes orchestrator, enabling seamless management and scalability. This platform allows for the straightforward addition of new sensing, diagnosis, and testing applications as needed, offering flexibility and adaptability to evolving requirements. To train the CNN model, a workstation PC equipped with a Graphics Processing Unit (GPU) retrieved the captured data stored in the Redis container. The performance of the workstations is shown in Table 1. Once the training process was completed, the trained CNN model was transferred to the filter shift estimation container. This allowed for real-time detection of the filter shift using the trained model, ensuring an efficient and accurate performance. Figure 8: Sensing system configuration: (**a**) Docker containers for real-time detection; (**b**) 2D histogram generated by TRDS. Figure 9: Mapping points of BERs.