## 3 Methodology
### Atmospheric Duct Interference Mitigation
These special subframes can be dynamically configured or de-configured based on the decisions made by the ADI mitigation models. The specific configuration of these subframes within the ADI mitigation system is presented in Table 9 [29]. As in the case of the prediction models, the dataset contains interference values for the 12 subcarriers of the zeroth physical resource block (PRB) in the TD-LTE network. Atmospheric duct interference (ADI) mitigation is performed individually on each subcarrier within this PRB. In TD-LTE systems, a single PRB comprises 12 consecutive subcarriers. The evaluation parameters presented in the Results and Discussion Section are derived from the first subcarrier of the zeroth PRB. The reported Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER) values correspond to the model outputs for this specific subcarrier. Both the SNR and BER are measured using the Remcom wireless Insite MIMO version (2022 release). Next, various classifiers, including the GB classifier, LSTM classifier, CNN classifier, ODGB classifier, SGD classifier, and Histogram-based Gradient Boosting (HGB) classifier, were investigated for ADI mitigation. Each classifier was tested using three distinct models with varying hyperparameters, as illustrated in Figure 7, where **x** represents the classifier (x \(\in\) {1, 2, 3, 4, 5, 6}) and **y** denotes the models with different hyperparameters (y \(\in\) {1, 2, 3}). Furthermore, for each classifier, the models with different hyperparameters were combined using ensemble learning to identify the best-performing classifier for ADI mitigation. In this approach, Model Y\({}_{\text{a}}\) and Y\({}_{\text{b}}\) of Classifier x are ensembled, and their combined feature output is passed through Model Y\({}_{\text{a}}\) of Classifier \(x\) in a second stage. This final stage is used to configure the guard period for interference mitigation, as shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Config. ID**} & \multicolumn{3}{c}{**Conventional Configuration Approach**} & \multicolumn{3}{c}{**Extended Configuration Approach**} \\ \cline{2-7}  & **DWPTS** & **GP** & **UPPTS** & **DWPTS** & **GP** & **UPPTS** \\ \hline C1 & 3 & 10 & 1 & 3 & 8 & 1 \\ \hline C2 & 3 & 9 & 2 & 3 & 7 & 2 \\ \hline C3 & 9 & 4 & 1 & 8 & 3 & 1 \\ \hline C4 & 8 & 4 & 2 & 8 & 2 & 2 \\ \hline C5 & 10 & 3 & 1 & 9 & 2 & 1 \\ \hline C6 & 10 & 2 & 2 & 9 & 1 & 2 \\ \hline C7 & 12 & 1 & 1 & 10 & 1 & 1 \\ \hline C8 & 11 & 1 & 2 & 8 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The configuration of the special subframes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **ODGB Model 1** & **ODGB Model 2** & **ODGB Model 3** \\ \hline \hline Scaler & Min-Max Scaler & Min-Max Scaler & Min-Max Scaler \\ \hline Dataset shuffled & Yes & Yes & Yes \\ \hline Gamma & 2 & 4 & 4 \\ \hline Max depth & 4 & 3 & 3 \\ \hline Minimum child weight & 2 & 3 & 4 \\ \hline Max delta step & 3 & 4 & 3 \\ \hline Sampling method & Uniform & Gradient-based & Uniform \\ \hline Lamda & 2 & 3 & 4 \\ \hline Tree method & Auto & Exact & Auto \\ \hline Process type & Default & Update & Default \\ \hline Max bin & 128 & 128 & 256 \\ \hline Average & Macro-average & Macro-average & Macro-average \\ \hline Learning rate & 0.001–0.048 & 0.001–0.048 & 0.001–0.048 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The parameters of the three different ODGB models.