# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 2 Related Works and Our Approaches
### Real-Time Monitoring of Digital Coherent Optical Signals by TRDS  
We propose a novel network monitoring approach which is Tapped Raw Digital Signal (TRDS) monitoring in digital coherent optical receivers. An optical signal transmitted through optical transmission links experiences various impairments, including chromatic dispersion, polarization mode dispersion, optical losses in connection points, optical fiber bending, noise from optical amplifiers, and optical spectrum deformation induced by optical filter shift in intermediate optical filters in ROADMs. Therefore, there will be some information on these impairments in the optical signal received in digital coherent receivers. In conventional optical receivers, we use various powerful signal processes to compensate for such impairments, as shown in Figure 3.  
For instance, chromatic dispersion can be compensated for using finite impulse response (FIR) filters applied in either the time domain or the frequency domain. Similarly, polarization mode dispersion can be mitigated using adaptive butterfly-type filters, which dynamically update their tap coefficients to address variations. However, from the perspective of detecting various impairments, such robust signal processing techniques may pose issues. This demodulation operation will erase the information contained in the received optical signal. Thus, we propose a novel approach where we tap digital signals just after ADC in optical receivers, as shown in Figure 3 [24].  
By monitoring the raw signal without any digital signal processing, we can expect to detect various impairments occurring in transmission lines.  
The actual configuration for TRDS is shown in Figure 4. The sampling phase is synchronized with the symbol rate of optical signal by the PLL circuit, as shown in the figure. To improve the accuracy of synchronization, we compensated for the known impairment caused by chromatic dispersion in optical transmission links. The tapped raw digital signals retain information about various impairments in their original form,  
Figure 3: Block diagram of coherent receiver.  
preserving essential details for further analysis. Therefore, we can expect to detect such impairments by analyzing the TRDS signals.  
By utilizing the above-mentioned symbol synchronized sampling technique, we have already succeeded in fault detection for various optical networks by applying machine learning to digital data about optical networks obtained from digital signal processing LSI. In [24], the detection of optical fiber bending with machine learning was reported, which extended the network monitoring capability from nodes to links. It can detect optical fiber bending without any monitoring tools such as an Optical Time Domain Reflectometer (OTDR). The authors used pre-demodulation constellation data from a digital coherent optical receiver for machine learning. They implemented the detection software on the Cassini WhiteBox [25]. Their results showed high detection accuracy in high Optical Signal-to-Noise Ratio (OSNR) conditions. Furthermore, they achieved high accuracy in optical fiber bending detection by incorporating post-demodulation data in addition to pre-demodulation data under low OSNR conditions, too. In [26], the authors experimentally confirmed real-time detection of optical cross talk noise in an optical network by applying machine learning to tapped raw optical signal data obtained from a digital coherent LSI for the first time. As a result, they achieved above 98% of accuracy by using a 25 Gbit/s DP-16 QAM optical signal.