# Going Deeper into OSNR Estimation with CNN
## 3 Experimental Result and Analysis
### Results and Analysis  
We trained OptInception and its variants in Figure 7. For variants with mini-Inception-FIR, the convolutional kernels were truncated to the lengths of 64, 32, and 16, respectively. The OptInception-AVG adds average pooling with a pooling size of eight in each branch following the convolutional layer. OptInception-Leaky adds one more layer into the hidden layer in the FC network and alter the ReLU function with the leaky-ReLU function. Moreover, Inception-v4 was modified to fit in with the 1D input format of raw data from ADC. The 1D version of Inception-v4 and OptInception-AVG performed poorly, as shown in Figure 7. Violently inputting our data into a popular CNN designed for pattern recognition did not seem to work. The failure of OptInception-AVG proves that pooling will drop much useful information at the beginning of the networks, as shown by the design principle mentioned above. Pooling always plays a vital role in integrating similar features, but not in an information extraction step. The rest learn better on the training dataset. Next, the estimation accuracy of OSNR on the test dataset depicted in Figure 8 reveals the generalization ability and feasibility in the reality of the networks. It is obvious that the modifications in OptInception-Leaky are not successful for its rigorous fluctuation and poorer and poorer generalization with training going on. In contrast, OptInception improves continuously and can finally reach quite a high level of accuracy of estimation in the test. The ones with mini-Inception-FIR do not deteriorate performance much, but are not stable in the early stages of training. OptInception-Pro trades much steadier learning improvement with more expensive learning prices. Metaphorically, OptInception is more like an intelligent student who learns fast with less time and energy but who  
Figure 6: Experimental setup for OSNR monitoring in proposed scheme. ECL: External Cavity Laser. Gain EQ: Gain Equalizer. AOS: Acousto-optic Switch. VOA: Variable Optical Attenuator. ITLA: Integrated Tunable Laser Assembly. OBPF: Optical Bandpass Filter; ICR: Integrated Coherent Receiver; EDFA: Erbium-doped Fiber Amplifier. also occasionally makes mistakes, while OptInception-Pro is a hardworking student who polishes up their score patiently, step by step. The following monitoring results are estimated by OptInception-Pro to exploit the potential of the proposed scheme, considering its steady learning process. In Figure 9, the estimation of the proposed OptInception-Pro is generally precise. However, degradation occurs with OSNR climbing because it becomes harder to measure the exact noise power from raw data when OSNR is higher. Nevertheless, this phenomenon does not affect its applications since it is more important to monitor the quality of transmission links in poor condition when the OSNR is always low in practice. Figure 10 shows the test performance on different symbol rates and modulations, with Figure 10a showing mean absolute error (MAE) and Figure 10b showing root-mean-square error (RMSE). The MAE remains only about 0.125 dB when RMSE is 0.246 dB. The general performance does not fluctuate much, so the proposed scheme is almost transparent to modulations and symbol rates as long as the combinations are trained thoroughly. The tolerance against chromatic dispersion is investigated in Figure 11 as well. The number of loops is controlled to acquire different CD. 28 Gbaud PDM-QPSK signals when 20 dB OSNR are tested. The errors of estimation basically have a weak relation  
Figure 8: Evaluation on the test dataset for OptInception and its variants. Figure 7: Training process for OptInception and its variants. The curves of training losses are smoothed by an exponential weighted (EW) function and presented in a logarithmic scale. When it comes to the effect of the convolutional layers in monitoring, we probed the weights in convolutional kernels again, as [25] did. For best demonstration, OptInception with mini-Inception-FIR was selected.