## 2 Related Works
### Machine Learning and Deep Learning Approaches to Mitigate ADI  
[PERSON] et al. [3] developed a machine learning model to predict and mitigate the atmospheric duct interference in the TD-LTE networks. They utilized a framework called alternating direction methods of multiplier to predict and mitigate the atmospheric duct interference in the TD-LTE networks. The framework uses a linear distributed Support Vector Machine (SVM) algorithm. The machine learning model uses meteorological and network-side datasets. The two datasets are converted to an interference map in the preprocessing stage. The meteorological dataset is collected from a radiosonde in Baoshan city in Shanghai province in China, and the network-side dataset is collected from the China Mobile operator. Kriging interpolation is used in the preprocessing stage. The variations in the modified refractivity in the low atmosphere are the major reason for the formation of the atmospheric ducts.  
[PERSON] et al. [13] developed an atmospheric duct interference mitigation system using convolutional neural networks. The network-side dataset is collected from the Shenzhen division primary carrier in a province in China. The convolutional neural network model contains three convolutional layers and two fully connected layers. The network-side dataset contains interference data from the Global System for Mobile Communications (GSM), Digital Enhanced Cordless Telephone, and TD-LTE networks. The spectral waterfall images are generated from the power spectral density plot and the time domain plot. The time value is plotted on the \(x\)-axis, and the frequency value is plotted on the \(y\)-axis. The test dataset of the mitigation system is collected from 10,000 TD-LTE network cells in a province in China. The experiment is performed for eight different cases.  
[PERSON] et al. [14] developed an atmospheric duct interference prediction system using machine learning approaches in the TD-LTE networks. The prediction system uses the Support Vector Machine, Random Forest, and K-Nearest Neighbor algorithms. The key idea of learning a decision tree is how to choose the optimal division attribute. This study used a Classification and Regression Tree (CART) decision tree in the Random Forest algorithm to predict the atmospheric duct interference in the TD-LTE networks. The CART decision tree applies the Gini index to select the optimal division attributes. The interference dataset is converted to an interference map. The network-side dataset is collected from the China Mobile operator. The atmospheric side data are collected from a radiosonde in a province in China. The run-time values of the different mitigation systems are compared in this research paper.  
A summary of the machine learning- and deep learning-based mitigation systems is given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Year**} & **Detection** & **Train** & \multirow{2}{*}{**Test Accuracy**} & \multirow{2}{*}{**Network**} \\  & & **Methodology** & & & \\ \hline [PERSON] et al. [13] & 2019 & CNN & - & 0.856 & LTE/Wi-Fi \\ \hline \multirow{3}{*}{[PERSON] et al. [14]} & \multirow{3}{*}{2017} & Random & \multicolumn{3}{c}{0.650 (4000 samples), 0.680} \\  & & Forest & - & (10,000 samples), & TD-LTE \\ \cline{1-1}  & & & & 0.700 (20,000 samples) & \\ \hline [PERSON] et al. [15] & 2020 & CNN & 0.990 & 0.977 & TD-LTE \\ \hline \multirow{3}{*}{[PERSON] et al. [3]} & \multirow{3}{*}{2017} & SVM & \multicolumn{3}{c}{0.680 (10,000 samples), 0.720} \\  & & SVM & - & (40,000 samples) & \\ \cline{1-1}  & & KNN & - & 2. 0.700 (10,000 samples), 0.710 & TD-LTE \\ \cline{1-1}  & & & & (40,000 samples) & \\ \hline [PERSON] et al. [16] & 2021 & LSTM & - & 0.984 & 5G (FR1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The machine learning- and deep learning-based approaches.