## 3 Experimental Results and Discussions
The proposed method achieved about 5 dB power penalty improvement compared with MLSE at the FEC threshold. The BER with the optical backplane was similar to the OB2B curve, which was similar to the MLSE case. During the experimental measurements, we found that for both the optical backplane case and the B2B case, the developed trend of the BER curves for mentioned scheme were similar, which is also consistent with Figure 6. It is worth mentioning that, for the NN equalization scheme case, error free transmission could be achieved for the B2B case at an RoP higher than \(-5.5\) dBm. Moreover, for the lower BER region, the greater the BER fluctuation was caused by the same number of error bits. For the bandwidth-limited system considered here, DFE could not meet the demand, and the introduction of the NN equalization algorithm became necessary. Compared to DFE and MLSE, the NN outperformed due its powerful modeling capabilities, which owed to the architecture of the multi-layer neural network and nonlinear activation function in each neuron. We also noticed that for a few points, increasing the RoP resulted in an slightly increase in the BER, and such BER fluctuation was mainly because the training phase of the neural network had a gradient descent direction, and the training result fluctuated to a certain extent. However, because the neural network obviously improved the transmission performance, this phenomenon did not affect the significance, even under such fluctuation; detailed investigations around it will be conducted in the future works. During the measurements, the delay spans and taps exhibited an impact on the performance of the proposed algorithm. Thus, the impacts of the above factors were further studied, with the results shown in Figure 8. We first fixed the span at 16 and then changed the size of the tap. The number was determined by using the data of 80 Gb/s when the RoP is \(-10.5\) dBm. As shown in Figure 8a, as the number of delay taps increased, the equalization performance improved until a point of a BER floor region where a further increase in the complexity of the equalizer was unnecessary. In addition, the performance of the system under different span and tap combinations was also measured, as shown in Figure 8b. The BER performance remained basically unchanged when the number of delay spans was over 1. The same process was repeated, and similar phenomena were obtained when the number of taps was 8, 12 and 16. Thus, the NN equalization scheme was performed under 1 delay span and 16 taps in the present work. It is found in Figure 9 that for MLSE and the proposed NN method, the BER tended to further decrease with the further improvement of the RoP. However, due to experimental system limitations, the RoP could not be further increased. Hence, we studied the performance limits of the proposed algorithm by increasing the bit rate. The system performance at 90, 100, 120, and 140 Gb/s with an optical backplane was investigated, with the results given in Figure 9. Due to the similar BER performance of B2B and with the optical backplane case, BER curves with only the optical backplane are plotted in Figure 9 in order to make a clear comparison. Considering that 80 Gb/s was already beyond the capability of the DFE, only MLSE was applied in the following measurements for the comparison. The trend of the curves was similar to that of 80 Gb/s. When the bit rate was over 90 Gb/s, MLSE could meet the FEC limit anymore in the range of the experimental RoP, while the proposed NNcase still displayed obvious potential. For the bit rate of 100 Gb/s, the proposed method achieved the adopted HD-FEC requirement at an RoP of around \(-8\) dBm. For the bit rate 120 Gb/s, as shown in Figure 9, the BER performance of the proposed scheme could be lower than the FEC limit at the RoP of \(-4\) dBm. When the rate increased to 140 G, the NN algorithm could not make the BER lower than the FEC threshold, nor could it make the BER slightly higher than the FEC threshold at \(-2.5\) dBm.