## 3 Experimental Results and Discussions
The experimental results showed that under the above bandwidth-constrained transmission conditions, the performance of the NN algorithm was better than that of MLSE, especially for the bit rate over 90 Gb/s. In summary, we sorted out the data and plotted the BER as a function of the bit rate for the RoP in Figure 10 at a configuration such that the optical backplane was connected to the optical link. Figure 10a is for the MLSE case and Figure 10b is for the NN equalization case. Both the 7% HD-FEC  
Figure 4:0 The BER performance under (**a**) a different number of taps and (**b**) a different number of spans. Figure 4:1 The curves of the BER versus the RoP from 90 to 140 Gb/s with an optical backplane. and 20% soft-decision forward error correction (SD-FEC) limits are depicted in the measurement curves. From Figure 10a, it can be seen that the BER was below the 7% HD-FEC at 80 Gb/s only when the RoP was higher than \(-4.5\) dBm. If a 20% SD-FEC is used, when the RoP is higher than \(-4.5\) dBm, 100 Gb/s transmission could be achieved, which also means that the complexity of the decoding calculation was increased. As shown in Figure 10b, when the RoP was higher than \(-6.5\) dBm, 100 Gb/s transmission could be achieved under the 7% HD-FEC. When the RoP was higher than \(-4.5\) dBm, 140 Gb/s could be transmitted under the 20% SD-FEC. The gross maximum achievable rate using MLSE and NN equalization under different RoPs is presented in Tables 1 and 2 for the 7% HD-FEC case and the 20% SD-FEC case, respectively. This provides an essential reference for future system design in different scenarios. Furthermore, the complexity of the adopted MLSE and neural networks are compared in in Figure 4:2 The number of multipliers was used to represent the complexity of the algorithm. The method for calculating the complexity of MLSE was the same as that in [30], and the method for the NN in [31] was adopted here. For an ANN, \(N_{ep}\) denotes the number of completed epochs required for training; \(n_{i}\), \(n_{hi1}\), \(n_{hid2}\) and \(n_{0}\) are the number of neurons in the input, first hidden, second hidden, and output layers, respectively; for MLSE, \(M\) and \(L\) represent the number of M-ary modulations and memory lengths of MLSE, respectively. By calculation, the number of multipliers of MLSE in this paper was more than that of the NN. It is also worth mentioning that during our experimental measurements, we found that the use of a single hidden layer and the setting of the hidden node as 16 allowed for the best performance. However, when we further increased the number of layers and nodes, the complexity increased, but the performance was not significantly improved. For the current system configuration, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**RoP**} & \(\mathbf{-2.5}\) & \(\mathbf{-3.5}\) & \(\mathbf{-4.5}\) & \(\mathbf{-5.5}\) & \(\mathbf{-6.5}\) & \(\mathbf{-7.5}\) & \(\mathbf{-8.5}\) & \(\mathbf{-9.5}\) \\  & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** \\ \hline MLSE & 86 & 83 & 81 & \(<\)80 & \(<\)80 & \(<\)80 & \(<\)80 & \(<\)80 \\ NN & 137 & 131 & 115 & 111 & 109 & 92 & 85 & \(<\)80 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The measured gross maximum data rate (Gb/s) at the 7% hard-decision forward error correction (HD-FEC) limit. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**RoP**} & \(\mathbf{-2.5}\) & \(\mathbf{-3.5}\) & \(\mathbf{-4.5}\) & \(\mathbf{-5.5}\) & \(\mathbf{-6.5}\) & \(\mathbf{-7.5}\) & \(\mathbf{-8.5}\) & \(\mathbf{-9.5}\) \\  & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** & **(dBm)** \\ \hline MLSE & 105 & 103 & 102 & 94 & 84 & 83 & \(<\)80 & \(<\)80 \\ NN & \(>\)140 & \(>\)140 & \(>\)140 & 133 & 128 & 112 & 103 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The measured gross maximum data rate (Gb/s) at the 20% SD-FEC limit.