## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.5 The Results of the SGD Classifier-Based ADI Mitigation System  
The evaluation parameters of the SGD classifier-based ADI mitigation models are given in Table 27. The results are given for both scenarios one and two. The BER and SNR of the SGD-based ADI mitigation systems were evaluated at the receiver side for different learning rates. The results are presented in Table 28.  
In Scenario One, Model 1 achieves a classification accuracy of 0.62 with a learning rate of 0.008, and slightly improves in Scenario Two with an accuracy of 0.65 at a learning rate of 0.012. Precision and recall for Model 1 in both scenarios are moderate, but the F1 scores suggest an imbalance in precision and recall. Model 2 and Model 3 exhibit similar trends, with Model 2 achieving better recall and precision in Scenario Two (0.72 precision, 0.69 recall), while Model 3's performance is slightly lower overall. Model combinations, such as Models 1 and 2, show improved performance with higher accuracy (0.69), better F1 \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & & \multicolumn{3}{c}{**BER**} & \multicolumn{3}{c}{**SNR**} \\ \hline  & & \multicolumn{3}{c}{**Conventional**} & \multicolumn{3}{c}{**Extended**} & \multicolumn{3}{c}{**Conventional**} & \multicolumn{3}{c}{**Extended**} \\  & & \multicolumn{3}{c}{**Configuration**} & \multicolumn{3}{c}{**Configuration**} & \multicolumn{3}{c}{**Configuration**} & \multicolumn{3}{c}{**Configuration**} & \multicolumn{3}{c}{**Configuration**} & \multicolumn{3}{c}{**Configuration**} \\  & & \multicolumn{3}{c}{**Approach**} & \multicolumn{3}{c}{**Approach**} & \multicolumn{3}{c}{**Approach**} & \multicolumn{3}{c}{**Approach**} & \multicolumn{3}{c}{**Approach**} & \multicolumn{3}{c}{**Approach (dB)**} & \multicolumn{3}{c}{**Approach (dB)**} \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Classifier** \\ **ODGB** \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} **Coventional** \\ **Configuration** \\ **Approach** \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} **Extended** \\ **Configuration** \\ **Approach** \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} **Coventional** \\ **Configuration** \\ **Approach** \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} **Extended** \\ **Configuration** \\ **Approach** \\ \end{tabular} } \\  & & & & & & & & & & & & & & & \\ \hline Model 1 & 0.004 & 0.012 & 0.005 & 0.004 & 0.005 & 0.004 & 0.004 & 0.012 & \(-13.90\) & \(-12.80\) & \(-13.50\) & \(-11.80\) \\ \hline Model 2 & 0.008 & 0.016 & 0.003 & 0.004 & 0.004 & 0.003 & 0.008 & 0.016 & \(-12.60\) & \(-12.30\) & \(-11.90\) & \(-11.90\) \\ \hline Model 3 & 0.012 & 0.008 & 0.005 & 0.004 & 0.004 & 0.004 & 0.012 & 0.008 & \(-13.30\) & \(-13.40\) & \(-13.70\) & \(-12.90\) \\ \hline Model 1 and 2 & 0.016 & 0.012 & 0.002 & 0.005 & 0.002 & 0.005 & 0.016 & 0.012 & \(-11.30\) & \(-14.20\) & \(-11.40\) & \(-14.10\) \\ \hline Model 2 and 3 & 0.016 & 0.028 & - & - & - & - & 0.016 & 0.028 & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 26: The Bit Error Rates of the ODGB classifier-based mitigation systems with different learning rates. scores (0.34), and moderate log loss (0.29-0.25), indicating improved model balance and reliability with multiple configurations. In Table 28, the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) were evaluated under different learning rates and configurations. The results show that increasing the learning rate leads to a lower BER and an improved SNR, especially in the extended configuration. For instance, in Model 1, the BER decreases from 0.012 to 0.004, and the SNR improves from \(-11.30\) dB to \(-9.30\) dB when using data from all 10 base stations. Similarly, Model 2 shows a reduction in BER (0.003 to 0.002) and an increase in SNR (from \(-12.40\) dB to \(-10.10\) dB) with higher learning rates.