## 3 Experimental Verifications and Discussions
### Experimental Setup and Results Discussions  
The experimental block diagram is shown in Figure 3. First, we need to generate a random code instead of PRBS. Note that PRBS [32; 33] cannot be the sequence generator because the neural network will overestimate the BER performance [34]. Before entering the neural network, in order to facilitate the neural network training, we map it into one-hot vector. The waveform mapped by the neural network is upsampled by N times, then sent to the LPF, and then sent to the RFA after DAC conversion to obtain the amplified waveform. Before entering the MZM, the MZM needs to be adjusted to the linear working area, usually set to be in the middle, between the highest voltage and the lowest voltage, due to the MZM instrument, and the optical power usually has a certain deviation. The input optical power is 5 dBm, and the output optical power will hover around \(-7\) to \(-8\) dBm. The fiber channel uses SSMF. As the transmission distance increases up to 55 km, adding the Erbium Doped \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline M & 16 \\ n & 12 \\ Sampling rate & 84 GSa/s \\ Symbol rate & 12 GSa/s \\ Information rate & 4 bit/symbol \\ LPF bandwidth & 33 GHz \\ DAC/ADC ENOB & 4 \\ Fiber dispersion parameter & 17 ps/nm/km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Fiber Application Amplifier (EDFA) in the middle of the channel is necessary. Thermal noise caused by laboratory equipment has a strong impact on the waveform at the distance of more than 55 km. Although EDFA introduces amplifier spontaneous emission (ASE) noises, resulting in an Optical Signal Noise Ratio (OSNR) decrease, it amplifies the attenuated signal so that output symbols are able to be recognized by the neural network or Adaboost algorithm. The process of DSP is as follows. First, we perform cosine filtering, time recovery, dispersion compensation, and frame synchronization on the output waveform, so that the obtained waveform can correspond to the input of the transmitter one by one. Then, the Adaboost algorithm block enters for training. The method used for bit error rate is consistent with Figure 4. Note that, in contrast to Reference [17], the tunable dispersion module is not used to control the dispersion offset. Instead, a dispersion compensation algorithm is used in the DSP to deal with this problem. After the optical signal is converted into an electrical signal through PIN+TIA at the output end, the final result is obtained through ADC conversion. Due to the limitation of laboratory equipment, the oscilloscope we used cannot reach the sampling rate of 84 Gb/s, and the output waveform needs to be resampled during output. As shown in Figure 4, the signal before and after passing through the channel will be slightly distorted and offset due to the existence of the fiber dispersion. For ANN with the strict one-to-one correspondence, the offset has a serious impact on the training. Therefore, in order to solve this problem, instead of downsampling at the receiver, the acquired waveform is processed directly by selecting one sampling point from every 7 sampling points as downsampling at this time, so that the offset is relatively small on the DSP, and BER is improved. It can be seen that, although it has some losses, it can be acceptable. Although the signal received at the receiver is somewhat damaged, its waveform is still very clear. This is why we process the entire waveform instead of picking out n points. Before deciding the parameters of the AdaBoost algorithm, we first consider the influence of the received optical power on the system. As shown in Figure 5, we choose to  
Figure 4: The influence of the PD converted signal after passing through the channel. Figure 3: Training process of the Adaboost algorithm. measure the optical power at 35 km to show its versatility. Since we have already debugged MZM in the linear working area, the only one that can change the optical power is the laser transmitter.