## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.6 The Results of the HGB Classifier-Based ADI Mitigation System  
The results of the HGB classifier-based ADI mitigation system are presented in Table 29, which outlines the evaluation parameters for both Scenario One and Scenario Two. Additionally, the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) of the HGB-based ADI mitigation systems were assessed at the receiver side under varying learning rates, with the findings shown in Table 30.  
In Scenario One, Model 1 achieves an accuracy of 0.62 with a learning rate of 0.004, while in Scenario Two, the accuracy improves slightly to 0.64 when the learning rate is increased to 0.024. Precision values are moderate, ranging from 0.65 to 0.72, with recall varying between 0.62 and 0.67 across different models. Notably, Model 3 shows the best performance in Scenario Two, with the highest recall of 0.72 and precision of 0.71, achieved at a learning rate of 0.048. The combination of Model 2 and Model 3 in Scenario Two performs well, achieving an accuracy of 0.70 with a relatively low log loss of 0.12, suggesting a better balance between precision and recall compared to individual models. In Table 30, the evaluation of the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) reveals the effect of varying learning rates on system performance. As the learning rate increases, the BER consistently decreases, indicating that higher learning rates lead to better error mitigation. For instance, in Model 1, the BER improves from 0.024 to 0.002 when \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
**Classifier** & **Learning Rate** & **Conventional** & **Extended** & **Configuration** & **Learning Rate** & **Conventional** & **Extended** & **Configuration** & **Extended** \\  & & & **Approach** & **Approach** & & & & **Approach (dB)** & **Approach (dB)** & **Approach (dB)** \\ \hline
**Classifier** & **Learning Rate** & **Conventional** & **Extended** & **Configuration** & **Configuration** & **Configuration** & **Configuration** & **Configuration** & **Approach (dB)** & **Approach (dB)** \\ \hline Model 1 & 0.004 & 0.024 & 0.003 & 0.002 & 0.004 & 0.003 & 0.004 & 0.024 & \(-\)11.40 & \(-\)10.30 & \(-\)11.30 & \(-\)09.90 \\ \hline Model 2 & 0.016 & 0.048 & 0.003 & 0.003 & 0.003 & 0.004 & 0.016 & 0.048 & \(-\)10.40 & \(-\)11.20 & \(-\)10.40 & \(-\)11.20 \\ \hline Model 3 & 0.032 & 0.048 & 0.002 & 0.003 & 0.002 & 0.003 & 0.032 & 0.048 & \(-\)10.50 & \(-\)11.10 & \(-\)11.20 & \(-\)11.20 \\ \hline Model 1 and 2 & 0.024 & 0.024 & 0.004 & 0.005 & 0.004 & 0.005 & 0.024 & 0.024 & \(-\)12.40 & \(-\)13.50 & \(-\)13.50 & \(-\)14.40 \\ \hline Model 2 and 3 & 0.032 & 0.036 & 0.003 & 0.003 & 0.004 & 0.003 & 0.032 & 0.036 & \(-\)10.30 & \(-\)10.40 & \(-\)10.40 & \(-\)10.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 29: The evaluation parameters of the HGB classifier-based ADI mitigation models. \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
**Classifier** & **Learning Rate** & **Conventional** & **Extended** & **Conventional** & **Extended** & **Conventional** & **Extended** & **Configuration** \\
**HGB** & **Conventional** & **Conventional** & **Conventional** & **Approach** & **Approach** & **Approach** & **Approach (dB)** & **Approach (dB)** \\ \hline Model 1 & 0.004 & 0.62 & 0.70 & 0.67 & 0.31 & 0.28 & 0.024 & 0.64 & 0.72 & 0.62 & 0.33 & 0.28 \\ \hline Model 2 & 0.016 & 0.62 & 0.65 & 0.62 & 0.35 & 0.26 & 0.048 & 0.61 & 0.71 & 0.64 & 0.29 & 0.26 \\ \hline Model 3 & 0.032 & 0.61 & 0.66 & 0.62 & 0.31 & 0.18 & 0.048 & 0.67 & 0.72 & 0.69 & 0.30 & 0.10 \\ \hline Model 1 and 2 & 0.024 & 0.63 & 0.69 & 0.67 & 0.27 & 0.21 & 0.024 & 0.70 & 0.61 & 0.65 & 0.30 & 0.24 \\ \hline Model 2 and 3 & 0.032 & 0.68 & 0.69 & 0.65 & 0.20 & 0.23 & 0.036 & 0.70 & 0.70 & 0.69 & 0.22 & 0.12 \\ \hline \hline \end{tabular}
\end{table}