## 3 Experimental Verifications and Discussions
### Experimental Setup and Results Discussions
We will conduct experimental research on this in our future work. Secondly, for situations where the possibility is relatively small, we do not need to increase the depth to 10 to reach the traversal level in all cases. When the depth is set to 5, the bit error rate is similar to the neural network, and, at the same time, it is close to the HD-FEC threshold at about 65 km. When the depth is 5, we can see in Figure 8b that, under the same conditions, the BER of other methods is nearly one order of magnitude lower than that of AdaBoost. Although the other methods are simpler in training and calculation than the AdaBoost algorithm, they sacrifice too much BER performance. Therefore, we just compare ANN with the AdaBoost algorithm. Due to similar BER performance at 65 km, the comparison between ANN and the Adaboost algorithm is relatively equal. Under this situation, comparison is meaningful. In this situation, the Adaboost algorithm is not very complicated in terms of computational complexity. A low bit error rate index is exchanged for complexity. The computational complexity analysis [4] of the neural network and Adaboost algorithm is as follows. For the comparison method of computational complexity, we refer to Tables 2 and 3. The biggest advantages of the Adaboost algorithm over neural networks are shown in the table above. It can be seen from Table 2 that, compared to neural networks, the Adaboost algorithm has two orders of magnitude lower multiplication operations. On the contrary, it only adds some addition and subtraction operations, which is a breakthrough in complexity. This is also a good foundation for real-time transmission of information. Since the autoencoder is equivalent to learning and adjusting the channel, for real-time transmission, channel adjustment will inevitably bring about re-training. At this time, the complexity of training must also be taken seriously. Obviously, as shown in Table 2 (the algorithm complexity of Adam and the Adaboost weight adjustment is not considered here, and the complexity of Adam is much higher), the training also transfers most of the multiplication operations to the multiplication operations, which is beneficial to the  
Figure 8: The bit error rate performance of Adaboost between different depth of Decision Tree and ANN (**a**) and the bit error rate performance of Adaboost compared with different Trees with the same situation (**b**) (Number = 30, Depth = 5). complexity. The reduction in complexity paves the way for rapid learning of changes in channel conditions later. The above, Table 4 shows the speed difference between the two methods shown in ANN and Adaboost training and running. We use i7 7700 and Nvidia 2070 8 GB to train our system. Under the same conditions, each takes 100,000 one-hot vectors for training. Theoretically speaking, the ANN method should be more effective due to the use of TensorFlow-GPU [35] (the same problem is usually solved by GPU faster, and the general acceleration ratio is 3 to 10 times). There is no doubt that TensorFlow on the GPU definitely is achieved parallel by mini-batch in training. The only disadvantage of this algorithm is that it is transmitted to each other through PCIE during transportation so that a lot of time is spent on information, but, for complex operations, especially when FFT and other operations are added, we can often ignore the time spent on transportation. As far as the results are concerned, the Adaboost algorithm is obviously better than ANN in training and running time. The reason is that it saves a lot of multiplication operations. It is reflected in the fact that the equipment rarely uses multipliers, making the algorithm more efficient.