## 3 Methodology
### Atmospheric Duct Interference Mitigation
\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 8: The border values of the target classes. Figure 5: OFDM modulation and demodulation block diagram. **Introduction**  
The interference level data in the space domain is converted to an \(M\times N\) matrix \(X\). The following steps outline the process of generating the interference map using Kriging interpolation. **Definitions**:  
* \(lo_{i},la_{i},le_{i}\): Longitude, latitude, and interference value of the \(i^{th}\) measuring point. * \(lo_{\min},lo_{\max},la_{\min},la_{\max}\): Minimum and maximum values of longitude and latitude of the Jaffna district, Sri Lanka. * \(le_{\min},le_{\max}\): Minimum and maximum values of the interference level. * \(lo_{\text{step}}=\frac{lo_{\max}-la_{\min}}{M}\): Longitude step size. * \(la_{\text{step}}=\frac{la_{\max}-la_{\min}}{N}\): Latitude step size. The matrix \(X_{m,n}\) is defined as  
\[X_{m,n}=\frac{1}{K}\sum_{i=1}^{K}le_{i}\cdot A(lo_{i},la_{i})\]  
where  
\[m =0,1,2,\ldots,M-1\] \[n =0,1,2,\ldots,N-1\] \[A =[lo_{\min}+m\cdot lo_{\text{step}},lo_{\min}+(m+1)\cdot lo_{ \text{step}}]\times[la_{\min}+n\cdot la_{\text{step}},la_{\min}+(n+1)\cdot la_ {\text{step}}]\] \[A(lo,la) = \begin{cases}1,&\text{if $lo\in A$ and $la\in B$}\\ 0,&\text{otherwise}\end{cases} \]  
**Kriging Interpolation**  
Kriging interpolation is used to estimate unknown points from known points. The estimation of the point \((x_{0},y_{0})\) is given by  
\[Z_{0}=\sum_{i=1}^{n}\lambda_{i}Z_{i}\]  
where  
* \(Z_{0}\): Estimated value at \((x_{0},y_{0})\)
* \(\lambda_{i}\): Weight, calculated to minimize \(\text{Var}(Z_{0}-\bar{Z})\) such that \(\mathbb{E}(Z_{0}-\bar{Z})=0\)  
**Algorithm**  
```
1:Input: The set of longitude, latitude, and interference levels for each base station (BS): \(lo_{i},la_{i},le_{i}\)
2:Output: Interference map
3:Determine \(M\) and \(N\). Usually, \(M\times N\) is \(\frac{1}{10}\) of the total number of base stations (BS) approximately. 4:\(X_{m,n}=\frac{1}{K}\sum_{i=1}^{K}le_{i}\cdot A(lo_{i},la_{i})\)
5:Generate \(X\)
6:for each \(x_{i,j}\in X\), if \(x_{i,j}=0\) and \((i,j)\) in the province range do
7: Use the nearest 30 points of \((i,j)\) to perform Kriging interpolation to get \(x_{i,j}\)
8:endfor
9:for each \(x_{i,j}\in X\), if \(x_{i,j}=0\) and \((i,j)\) out of the province range do
10: Set \(x_{i,j}=le_{\min}\)
11:endfor
12:Render \(X\) using a heatmap
13:return Interference map
```  
**Algorithm**  
Moreover, a modified formulation of the SNR is employed to better emphasize the influence of interference components within the received signal, as in Equation (1). Under  
Figure 6: The data preprocessing algorithm [12; 28]. this formulation, more negative SNR values indicate a higher signal power relative to interference (noise). This approach enables the model to effectively prioritize and monitor scenarios with pronounced interference characteristics, which are particularly important in ADI conditions in TD-LTE networks. \[SNR=-10\text{log}_{10}\frac{Signal\ Power}{Noise\ Power} \tag{1}\]  
The mitigation models were also developed under two distinct scenarios. In Scenario One, feature data were collected from all ten base stations located within the Jaffna district. In Scenario Two, data were obtained exclusively from a single base station--specifically, the Jaffna Town base station. The geographical coordinates of all ten base stations in the Jaffna district are provided in Table 5.  
Further, there are three special subframes in TD-LTE communication frames: the uplink pilot time slot (UPPTS), the downlink pilot time slot (DWPTS), and the guard period (GP).