## 3 Methodology
### Atmospheric Duct Interference Prediction  
The prediction of ADI strength is critical for proactive interference management in wireless communication networks. ML and DL models have proven effective in forecasting ADI by leveraging both atmospheric and network-side features. In this study, two prediction approaches were developed and evaluated, differing in the number of features used from the network side while sharing common atmospheric parameters. Both approaches incorporate three key atmospheric features: temperature, pressure, and humidity, which are sourced from the Visual Crossing weather monitoring base station. These features play a crucial role in determining atmospheric refractivity profiles, which directly influence the formation of ducting layers. The first approach utilizes eight network-side features, whereas the second approach expands this to fifteen network-side features. Common network-side parameters include uplink power values obtained from the operational data of Dialog Axiata PLC, a major mobile network operator in Sri Lanka [7]. The inclusion of a broader feature set in the second approach aims to enhance the model's sensitivity to subtle interference-related variations across the network. Both prediction models are trained to classify the strength of ADI into six target classes, which represent different levels of interference severity. The categorization of these classes is detailed in Table 4, serving as a structured framework for evaluating prediction performance and guiding subsequent interference mitigation strategies. This dual-approach design enables comparative analysis of model accuracy and robustness based on feature richness, ultimately contributing to the development of more adaptive and scalable ADI prediction solutions in TD-LTE and 5G environments. The prediction models are developed in two scenarios. In the first scenario, the features are collected from all ten base stations in the Jaffna district. In the second scenario, the features are collected from only one base station, which is the Jaffna Town base station. The coordinates of the ten base stations in the Jaffna Town district are given in Table 5.  
The Support Vector Machine (SVM) model was configured with 11 input features and trained using four different kernel functions--linear, radial basis function (RBF), polynomial, and sigmoid. It employed five-fold cross-validation with a learning rate of 0.001, targeting classification into six ADI severity levels. Similarly, the Random Forest model was evaluated in two configurations. The first model used 100 estimators, while the second employed 10 estimators with the entropy criterion. Both versions used the same input features and training strategy as the SVM. The Long Short-Term Memory (LSTM) model was applied in two distinct architectures. In the first approach, it consisted of three layers and was trained for 50 epochs using 11 features. The second approach expanded the feature set to 18 and adopted a four-layer architecture, comprising an input layer (18 neurons), two hidden layers (20 neurons each), \begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Classes** & **Min Value** & **Max Value** \\ \hline Class A & \(-112.00\) dB & \\ \hline Class B & \(-116.00\) dB & \(-112.01\) dB \\ \hline Class C & \(-120.00\) dB & \(-116.01\) dB \\ \hline Class D & \(-124.00\) dB & \(-120.01\) dB \\ \hline Class E & \(-128.00\) dB & \(-124.01\) dB \\ \hline Class F & & \(-128.01\) dB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The interference range of the target classes. and an output layer (6 neurons). It used the Adam optimizer and mean squared error (MSE) loss, with a learning rate ranging from 0.001 to 0.048. The convolutional neural network (CNN) was also developed in two approaches. The first model utilized 11 features and consisted of three layers with ReLU activations in the initial two and a SoftMax activation in the final layer. The second CNN model employed 18 features and a four-layer structure, mirroring the configuration of the advanced LSTM model.