## 3 Methodology
### Atmospheric Duct Interference Mitigation  
Numerous ADI mitigation systems have been developed over the past decade. However, many of these systems exhibit limitations in terms of mitigation efficiency. These shortcomings highlight a clear research gap in the current state of ADI-related methodologies. To address this gap, a novel ADI mitigation system is proposed. An overview of the proposed framework is illustrated in Figure 2, outlining the key components and operational flow of the system [19; 20]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifiers** & **Classifier in Stage 1** & **Classifier in Stage 2** \\ \hline
1 & LSTM & SDG \\ \hline
2 & LSTM & GB \\ \hline
3 & LSTM & XGB \\ \hline
4 & LSTM & LSTM \\ \hline
5 & LSTM & CNN \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The structure of the cascaded prediction models. Figure 1: The cascaded ML and DL classifier-based prediction models. In the considered network scenario, there are two groups of base stations: aggressor base stations and victim base stations. The aggressor group comprises six base stations (m = 6), while the victim group consists of eight base stations (n = 8). This configuration is illustrated in Figure 3. The line-of-sight (LoS) channel, along with the channels affected by atmospheric duct interference (ADI), are associated with the victim base stations, highlighting the impact of interference propagation in the network [21; 22]. One of the key contributions of this research is the adaptive adjustment of the guard period--the time interval between uplink and downlink signals in TD-LTE networks--to mitigate atmospheric duct interference (ADI). By leveraging machine learning models, the system dynamically modifies the guard period in response to detected interference conditions. This approach enables real-time interference management, enhancing the robustness of TD-LTE communications. The structure of the uplink and downlink frames, both with and without atmospheric duct interference, is illustrated in Figure 4[21; 23]. The TD-LTE network uses OFDM to modulate the information-bearing signals in the carrier signal. Figure 5 shows the block diagram of the OFDM modulation and demodulation scheme. Figure 4: The TD-LTE system (**a**) without ADI and (**b**) with ADI [8]. Figure 3: Network setup with six aggressor and eight victim base stations. Figure 2: The proposed ADI detection and mitigation system. The guard interval between the uplink and downlink signals could be set at the OFDM transmitter block to remove the atmospheric duct interference in the received signal. The guard period configuration could be removed at the OFDM receiver block to identify the transmitted messages. An interference map is created in the data preprocessing stage. The data preprocessing stage is given in Figure 6. This algorithm converts spatial interference measurements into a matrix representation and utilizes Kriging interpolation to estimate unknown values. The method is applied to interference data collected from base stations over a specific district [24; 25]. We use three atmospheric side features and fifteen network-side features in the ADI mitigation systems. The three atmospheric side features of the ADI mitigation systems are atmospheric temperature, atmospheric pressure, and atmospheric humidity. The uplink power values of the first frame in the uplinks 0, 1, 2, 3, 4, 5, and 6 and the uplink power values of the last frame in the uplinks 0, 7, 8, 9, 10, 11, 12, and 13 are the fifteen network-side features of the ADI mitigation systems. The features are normalized between 0 and 1 [26; 27]. The target of the mitigation system contains six classes. The border values of the target classes are given in Table 8.