# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Combined Optical Filter Shift Detection and BER Evaluation  
To simultaneously monitor the optical filter shift and the time series variation in BER, we conducted an evaluation experiment in the configuration, as shown in Figure 21. We implemented container applications to monitor the BER at the tapping points, as shown in Figure 8. These applications work together with the optical shift detection container. Figure 21: Simultaneous acquisition of optical filter shift and BER. Figure 20: Evaluation result at 26 GHz with augmentation data (DS: 41,000). The summary results of simultaneous monitoring of BERs and DC-GAN-enhanced optical filter shift detection are shown in Figure 22. The SD-FEC BER for each optical filter shift stayed stable when the optical filter shift was within 26 GHz and showed a significant increase at \(\pm\)51 GHz. As for optical filter shift detection, we successfully detected it at as small as \(\pm\)26 GHz, where we there was no sign of abnormality in the BER. The BER is a fundamental measure for transmission quality; so, we can say that network services work well and in a normal state when there is no BER increase. In other words, the proposed technology made it possible to confirm that an optical filter shift had already occurred, even when the optical transmission line was in a completely normal state. The optical filter shift gradually increases with time; so, we can predict that there will be a failure resulting from the optical filter shift in ROADMs in the near future by utilizing the novel DC-GAN-enhanced optical filter shift detection. To realize a practical prediction system based on this idea, one of the options may be to accumulate and prepare sufficient data of temporal drift of the central optical frequency in ROADM nodes. Based on this information, we can estimate the residual time to encounter the failure. How to estimate the actual time needed to induce ROADM failures will be focused on in further research. In a conventional optical network, we have multiple ROADM nodes between the optical sender and receiver. By using our proposed technique, we can find the optical filter shift in a ROADM node among these ROADM nodes. How to identify which node has a problem will be focused on in a further study. On of the options will be to detune each optical filter with a very small amount, such as 26 GHz, in the opposite direction of the detected optical filter shift in the candidate ROADMs. When we find no optical filter shift sign from the detection container, the ROADM can be assumed to be experiencing failure. In this case, we only need one detection container in the receiver side; so, it will be a cost-effective solution. In general, precise control of absolute optical frequency is very hard. But relative detuning is conventional. For example, the optical frequency of filters is usually stabilized by temperature control of the relevant devices. Based on the derivative coefficient of optical frequency and temperature, we can detune frequency in some desired amount by changing the temperature. But it becomes very hard to anchor at an absolute optical frequency. However, such a detuning function will be available only in limited versions of ROADMs. So, a generic approach to realize a cost-effective solution will be the focus of further research. Figure 22: Joint diagram of SD-FEC BER variation and sensing results at each optical filter shift. In this evaluation experiment, we confirmed a specific network condition, as explained in the experimental setup. To ensure more reliability of our approach, we need experimental verification in various network conditions, including various OSNR conditions, modulation formats, FEC variations, and different network architectures, among others. These evaluations will be the focus of further studies. The computation time required for DC-GAN data augmentation is around 30-60 min using a conventional GPU-enabled server.