## 3 Experimental Verifications and Discussions
### Experimental Setup and Results Discussions
As the depth of Tree increases, the repetitiveness of the waveform decision is easier to solve, and the decision distinction becomes more effective. Therefore, it can be said that the  
Figure 6: The bit error rate performance of the Adaboost algorithm, Random Forest, Extra Tree, and Voting Tree with the number of Decision Trees (**a**) and the Adaboost algorithm with the number of Decision Trees between 25 km to 75 km (**b**) and the relationship of the Adaboost algorithm with distance and its number (**c**). Adaboost algorithm achieves the optimal solution at the same distance. Next, we analyze how to find the optimal depth point. As shown in Figure 7b, it can be seen that the depth has a greater impact on the bit error rate relative to the number of Decision Trees. The depth of the Decision Tree will also tend to be flat when it reaches a certain depth. The reason is that, as the depth increases, it tends to be too sensitive to the analysis of some small changes in the signal waveform, which is actually harmful to the signal classification. After training, the classifier often knows that there are actually not two signal waveforms. This not only causes misjudgment but also increases training resources and computation-al complexity, which is of no benefit to reducing computational complexity. After the comparison of the aforementioned, Figure 7b, the depth of the short distance to the middle distance is often concentrated between 4 and 6. The following figure will continue to compare. As shown in Figure 8, we first analyze the advantages and disadvantages between the neural network and Adaboost algorithm. For an autoencoder using a symmetrical neural network, it can be seen that it can still be lower than the adopted HD-FEC threshold for a long distance, and the output is relatively stable between 35 km and 65 km, but, at the distance of 75 km, the bit error rate will rise sharply because the inter-symbol interference caused by 75 km dispersion becomes more and more obvious, and the noise brought by the machine itself has gradually been able to compete with the power of its own signal. These are the reasons for the linear increase in the bit error rate. In addition, due to the usage of EDFA that amplifies the attenuated signal at more than 55 km, the output of 35-65 km is relatively stable as a result. Since the optical power emitted by the MZM has reached \(-7\) to \(-8\) dBm, the optical power obtained after 45 km has been lost to nearly \(-20\) dBm. At this point, although neural networks and Decision Trees can still recognize symbols, they are already at the limit. Then, we analyze and compare the depth of the Adaboost algorithm Decision Tree. In the case of the same number of Decision Trees, it is obvious  
Figure 7: The bit error rate performance of Adaboost, Random Forest, Extra Tree, Voting Tree with the depth of Decision Trees (**a**) and the Adaboost algorithm with the depth of Decision Trees between 25 km to 75 km (**b**) and bit error rate performance of the Adaboost algorithm with its depth (km) (**c**). that the depth of the Tree will have a positive impact on data classification. However, too much depth is not our expectation. Due to limited experimental equipment, we cannot collect data to verify the performance of BER at different distances in order to analyze its robustness. We should be able to conclude that the Adaboost algorithm will be a certain degree of robustness at different distances. The reason is that, due to the relatively large number of Decision Trees, the Adaboost algorithm has a certain degree of robustness at different distances. However, compared to ANN, the figure of the BER performance will appear steeper in more changeable distance. Instead, compared to BiLSTM [18], the BER performance will be much poorer due to having none of the memory function. At the cost of excellent BER performance, the computation is more complex than the method we proposed. At this time, the weighted decision of multiple Decision Trees is obviously problematic.