## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.4 The Results of the ODGB Classifier-Based ADI Mitigation System  
Table 25 outlines the performance metrics of the ODGB classifier models developed for ADI mitigation under both Scenario One and Scenario Two. In addition to classification accuracy and related parameters, the impact of varying learning rates on system performance was examined. Correspondingly, Table 26 presents the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) measurements obtained at the receiver end, offering further insights into the effectiveness of ODGB-based mitigation strategies.  
In Table 25, the classification accuracy, precision, recall, F1 score, and log loss of the models are compared under two scenarios. Scenario One shows that Model 1 achieves a classification accuracy of \(0.68\) with a learning rate of \(0.004\), and in Scenario Two, Model 1 maintains the same accuracy with a learning rate of \(0.012\). Precision values remain between \(0.65\) and \(0.66\) across models, indicating a moderate ability to correctly identify positive instances. Recall varies more significantly, with Model 1 in Scenario One having a recall of \(0.56\), while other models, like Model 2 in Scenario Two, achieve a recall of \(0.62\), indicating a better identification of positive instances. The F1 scores, which balance precision and recall,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Scenario One**} & \multicolumn{4}{c}{**Scenarios Two**} \\ \hline \multirow{3}{*}{**Classifier**} & \multirow{3}{*}{**ODGB**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} & \multirow{3}{*}{**Conventional**} \\  & & & & & & & & & & & & & \\ \cline{1-1} \cline{6-12} \cline{6-12} \cline{6-12} \cline{6-12} \cline{6-12} \cline{6-1  
range from 0.55 to 0.70, with some models exhibiting better overall balance. Log loss values vary between 0.18 and 0.25, suggesting a moderate degree of accuracy in prediction, with minimal fluctuation across different learning rates and models.  
In Table 26, the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) are examined at different learning rates for both conventional and extended configuration approaches. The BER shows a general decrease as the learning rate increases, which indicates better performance in terms of error reduction with higher learning rates. For example, in Model 1, the BER is lower when using a dataset collected from all ten base stations compared to just one base station. The SNR also improves with higher learning rates, particularly in the extended configuration. For instance, in Model 1, the SNR values range from \(-13.90\) dB to \(-11.80\) dB as the learning rate increases, showing improved signal quality under extended configurations.  
Overall, the results suggest that the ODGB-based mitigation system benefits from higher learning rates, leading to improved accuracy, reduced error rates (BER), and better signal clarity (SNR). However, the performance improvements are moderate and vary across different models and configurations, highlighting the need for further optimization and fine-tuning of learning rates for enhanced system performance.