## 3 Experimental Verifications and Discussions
### Experimental Setup and Results Discussions
We can see that, with the increase of optical power, the eye diagram gradually becomes clear. When the optical power is about \(-8\) dBm (Figure 5c), the eye diagram can gradually be divided into two levels. It can be seen that the best effect can be obtained when the linear working area of MZM is about \(-6\) dBm. At this time, the working zero point of MZM curve can be obtained when the received optical power is about \(-6\) dBm, when the BER tends to be flat. Therefore, we choose \(-6\) dBm. Because we use different transmission distance, we can only control the transmission optical power, so we can obtain the transmission optical power of 3.6 dBm. Adaboost algorithm uses a set of Decision Trees to solve the problem, two of which are worth noting, depth and the number of Decision Trees. In Figure 6a, BER performance by the Adaboost algorithm is much better than other Machine Learning methods since the Adaboost algorithm uses weight. The weight coefficient is used to determine the proportion of the classifier to determine the waveform, which is more conducive to the determination of the waveform. As shown in Figure 6b,c, using the control variable method, first, consider the comparison of the impact of the number of Decision Trees. In order to better reflect the advantages of the Adaboost algorithm in weak classifier planning, we use more Tree class methods for training as the receiver to receive the signal. We use different methods, such as Random Forest, Extra Tree, and Voting Tree, to compare with the Adaboost algorithm. Voting Tree adopts the voting mechanism, through voting on Extra Tree, Random Forest, and Decision Tree, to select the best classification result to obtain the lowest BER. It is obvious that, with the increase of the number of Trees, the change of bit error rate will tend to be flat. In addition, above that, in the Adaboost algorithm, compared with other methods, does not show advantages because it will produce over fitting phenomenon in  
Figure 5: (**a**) The bit error rate performance of the Adaboost algorithm, ANN with received optical power in the distance of 35 km (the number of Adaboost Decision Tree is 30, and its depth is 5), and the eye diagram in the received optical power of \(-11\) dBm (**b**), \(-8\) dBm (**c**), \(-6\) dBm (**d**). the training process, which leads to signal misjudgment. Other methods also have this problem. In order to solve this problem, the traditional method is to limit the number of leaf nodes and the threshold value of each layer of leaves, while, in the communication system, the characteristics of the signal are complex and uncertain There are no rules, so we usually make the depth of the Tree deeper to traverse all kinds of possible signal situations to reduce the occurrence of bit error rate. Next, we analyze how to find the optimal number of points. It can be seen that, as the number of Decision Trees increases, the bit error rate drops sharply when the number is less than 20. As the transmission distance increases, the critical point at which the bit error rate tends to level off gradually comes back. The reason is that the more Decision Trees there are, the more serious the over-fitting phenomenon will be, and the waveform cannot be correctly classified. Therefore, the redundant Decision Tree has no positive help for the reduction of bit error rate. Figure 6b shows the number of points at which each transmission distance tends to be flat. Considering the computational complexity and the best point for data, 30 is selected as the Number of Decision Trees point. Then, we consider the depth of the Decision Tree. As shown in Figure 7a, it can be seen that, with the increase of depth alone, there is still a great gap between other Machine Learning methods and the Adaboost algorithm, and the decline of the Adaboost algorithm tends to moderate at last because it has reduced the bit error rate to the lowest possibility.