## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.2 The Results of the LSTM Classifier-Based ADI Mitigation System  
The performance metrics of the LSTM classifier-based ADI mitigation models for both Scenario One and Scenario Two are summarized in Table 21. Additionally, Table 22 presents the BER and SNR values of the LSTM-based mitigation systems, measured at the receiver side across various learning rates.  
\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}{**Scenario Two**} \\ \hline
**Classifier** & & & & & & & & & & & & \\
**LSTM** & & & & & & & & & & & & \\ \hline Model 1 & 0.012 & 0.62 & 0.67 & 0.62 & 0.55 & 0.24 & 0.024 & 0.62 & 0.61 & 0.63 & 0.48 & 0.16 \\ \hline Model 2 & 0.004 & 0.61 & 0.69 & 0.66 & 0.48 & 0.21 & 0.024 & 0.62 & 0.68 & 0.53 & 0.38 & 0.24 \\ \hline Model 3 & 0.008 & 0.63 & 0.74 & 0.58 & 0.45 & 0.11 & 0.016 & 0.64 & 0.67 & 0.62 & 0.47 & 0.15 \\ \hline Model 1 and 2 & 0.012 & 0.62 & 0.60 & 0.61 & 0.33 & 0.11 & 0.016 & 0.65 & 0.70 & 0.66 & 0.49 & 0.23 \\ \hline Model 2 and 3 & 0.028 & 0.68 & 0.62 & 0.68 & 0.50 & 0.10 & 0.012 & 0.65 & 0.67 & 0.61 & 0.39 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 21: The evaluation parameters of the LSTM classifier-based ADI mitigation models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**SER**} & \multicolumn{4}{c}{**SNR**} \\ \hline
**Learning Rate** & \multicolumn{2}{c}{**Conventional**} & \multicolumn{2}{c}{**Extended**} & \multicolumn{2}{c}{**Conventional**} & \multicolumn{2}{c}{**Extended**} \\  & & \multicolumn{2}{c}{**Configuration**} & \multicolumn{2}{c}{**Configuration**} & \multicolumn{2}{c}{**Configuration**} & \multicolumn{2}{c}{**Approach**} & \multicolumn{2}{c}{**Configuration**} & \multicolumn{2}{c}{**Approach (dB)**} & \multicolumn{2}{c}{**Approach (dB)**} \\ \hline
**Classifier** & & & & & & & & & & & & \\
**LSTM** & & & & & & & & & & & & & \\ \hline Model 1 & 0.012 & 0.024 & 0.002 & 0.004 & 0.003 & 0.004 & 0.012 & 0.024 &  
The performance evaluation of the LSTM-based ADI mitigation system reveals consistent results across both test scenarios. In terms of classification metrics, Model 3 exhibited substantial precision in both scenarios, with a notably low log loss, suggesting confident and accurate predictions. Among all combinations, the Model 2 and Model 3 ensemble achieved the highest accuracy (0.68) in Scenario One and maintained solid recall and F1 scores, making it a strong candidate for effective ADI detection and mitigation. Scenario Two showed slightly better overall balance in precision and recall across different models, especially for the Model 1 and 2 ensemble.  
The BER and SNR analysis in Table 22 supports the classification performance. The extended configuration approach consistently outperformed the conventional method, showing lower BER values and higher SNR values across most models and learning rates. Notably, Model 2 and Model 3 maintained low BER and high SNR, especially when data were sourced from all ten base stations, underscoring their robustness in diverse deployment conditions. Overall, the results indicate that LSTM classifiers--especially when ensembled--are highly effective in mitigating ADI under varying learning rates and data sources.