## 6 Future Research Scope
### Spatial Rainfall Distribution Along Slant Link  
Using spatial interpolation techniques, the unknown rainfall rate values for the locations of unavailable data are estimated. The distance between each known point (with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Ref.**} & **Used/Validation** & \multirow{2}{*}{**Remarks about Validation**} & **Complexity** & \multirow{2}{*}{**Constraints**} \\  & & **Databank** & & **Level** & \\ \hline \multirow{8}{*}{PHY} & [37] & ✓ & Validated at Lee, Papua New Guinea & low & — \\  & & Satellite: validated & & & The probabilities of incidence \\  & & through CCIR & & & and mean precipitation for cells \\  & [94] & procedure. Terrestrial: & 35 terrestrial paths & verified & & and debris are difficult to \\  & & around the world & & & determine \\ \cline{1-1}  & [95] & Experimental dataset & The experimental results show that the method & & medium & Needs 1 min rainfall rate \\  & & & show less error probability. & & \\  & & & Although the model does not outperform the & & & The correctness is limited by the \\  & & existing ITU-R model (approximately 2.4\% error), & & local values of input parameters \\  & & & it supports a few additional facilities (e.g., site & & like the melting layer and the \\  & & diversity) and takes account of the interference & & rain plateau value, which might \\  & & & due to hydrometer scattering & & not be available everywhere \\ \hline \multirow{8}{*}{FADE} & [96] & Experimental dataset & Validated with & low & — \\  & & & Validated in Kolkata, India, with experimental & & To use fade margin data it needs \\  & & & set-up & & to remove tropospheric \\  & & & set-up & & scintillation \\ \cline{1-1}  & [99] & Only experimental & The resulted output agrees with the measured & & The method was tested only at \\  & & dataset & standard deviation of attenuation & & 10.982 GHz \\  & [98] & NM & Validated in [140; 141] & low & Applicable for limited elevation \\ \hline \multirow{2}{*}{LB} & [100] & Durban, South Africa & The model was not validated with standard & — & The model was not tested with \\  & & & DSDB3 or CCIR rain databanks & — & established well-known rain \\ \hline \hline \end{tabular} EMPI: empirical, STAT: statistical, \(+\) it needs to solve at least seven equations. PHY: physical, FADE: fade slope, LB: learning-based rain attenuation model, \({}^{\dagger}\) it needs to solve ten equations.  
\end{table}
Table 9: _Cont._spatial coordinate \((x_{i},y_{i},z_{i})\) and the corresponding attribute is \(P_{i})\), where \(i\in N\) and the unknown point (with a spatial location of \((x_{0},y_{0},z_{0})\)) is \(d_{i}(x,y,z)\). IDW is used to estimate the property value of each unknown point. Equation (73) represents the estimation procedure through the IDW method, where \(m\) is the power in the inverse distance power law [147].  
\[P_{R}=\sum_{i=1}^{N}\frac{P_{i}}{\left[d_{i}(x,y,z)\right]^{m}}\wedge\sum_{i=1 }^{N}\frac{1}{\left[d_{i}(x,y,z)\right]^{m}} \tag{73}\]  
where distance is expressed as:  
\[d_{i}=\sqrt[\sqrt[]{|x_{0}-x_{i}|^{p}+|y_{0}-y_{i}|^{p}+|z_{0}-z_{i}|^{p}} \tag{74}\]  
The value \(p\) depends on the way the distance is considered. For example, in Euclidean distance, the value of \(p\) is given by 2. The IDW technique has been tested for a terrestrial link [148; 149], but it has not been tested for rainfall estimation in slant links. Some other methods include modified inverse distance weighting [150; 151], correlation coefficient weighting [150], multiple linear regression [152], and artificial neural networks [153], which can be examined for estimating the missing rainfall rate data. Now, for slant link rainfall estimation, research may be carried out based on these approaches to find suitable rainfall distribution along with the slant link.