# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 6 Experimental Results and Discussion  
In this section, experimental results are presented and discussed. All the parameters utilized in these experiments are fixed. As mentioned before, 1 km SSMF is utilized, while memory depths \(Q_{a},~{}Q_{b},~{}Q_{c}=Q=2\) and nonlinearity orders \(K_{a},K_{b},K_{c}=K=1\) are also fixed. In addition to this, biasing and threshold currents for biasing SM-VCSEL laser are also fixed.  
Figure 7 presents a comparison of experimental mean squared error (MSE) loss training for the NNs. It can be observed that the minimum MSE reached by the RoF NN model is 0.00189. This residue can be attributed to memory management and noise impairments in the data. Similarly, the DPD NN learns the predistortion profile for the RoF NN model. Once the learning converges, the DPD NN achieves an MSE approximately near to zero that equals \(1\times 10^{-5}\). It should be noted that this is a training loss for DPD with respect to the RoF NN model. On the basis of our previous findings in [6; 12], we chose memory depths \(Q_{a},~{}Q_{b},~{}Q_{c}=Q=2\) and nonlinearity orders \(K_{a},K_{b},K_{c}=K=1\).  
In Figure 8, the experimental findings are reported for ACPR for varying input powers. Since radio frequency (RF) input power can change with a change in application or scenario, it is of fundamental importance to check the effectiveness of the techniques used. The trend in Figure 8 confirms that the NN-based DPD method is performs better compared to GMP-based DPD. It can be noted that at a relatively higher RF input power of 5 dBm, the output ACPR is \(-25\) dBc without DPD, \(-35\) dBc with DPD-GMP and \(-40\) dBc with the DPD-NN method, respectively.  
Similarly, Figure 9 represents the Power Spectral Density (PSD), i.e., spectral regrowth of the received output signal with and without DPD for \(P_{IN}=0\) dBm. In the case of DPD, the two methodologies are shown. The DPD with GMP method is represented by the blue dashed line, while DPD-NN is shown using the green dash-dotted line. The output signal without DPD is shown by the red solid line. It can be observed that DPD-NN results in lower spectral regrowth with respect to GMP. Indeed, NN results in lower spectral regrowth as compared to GMP.  
Figure 7: Experimental results for comparison of experimental MSE training loss for the NNs.  
In addition to ACPR evaluation, EVM performance is evaluated, depicted in Figure 10, by sweeping the RF input power. It is evident that EVM results also show that DPD NN results in better reduction as compared to GMP. The dynamic range of the NN method is 5 dBs higher than the GMP method. This further confirms that the NN method outperforms Volterra-based methods. The overall results are summarized in Table 3.  
Figure 8: Experimental results for Adjacent Channel Power Ration (ACPR) results versus radio frequency input power for output with DPD-NN, DPD-MP, DPD-GMP and without DPD.  
Figure 9: Spectral regrowth for output with DPD-NN, DPD-GMP and without DPD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **ACPR (dBc)** & **EVM (\%)** \\ \hline No DPD & \(-25\) & \(8\) \\ \hline MP & \(-35\) & \(2.2\) \\ \hline GMP & \(-40\) & \(2\) \\ \hline NN & \(-50\) & \(1.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison among DPD methods for 0 dBm input power.