## 2 Proposed ANN-Based PL Model  
Numerous empirical and standardized models regarding PL prediction have been proposed, among which the third generation partnership project (3 GPP) and CI models are particularly renowned. The CI model enhances PL prediction accuracy by introducing the PL exponent (PLE) based on the free-space PL (FSPL) model, which can be formulated as  
\[PL_{\text{CI}}(f,d)=10n\text{log}_{10}(d/d_{0})+PL_{\text{FS}}(f,d_{0})+X_{ \lambda}^{\text{CI}} \tag{1}\]  
where \(d\) and \(f\), respectively, represent the propagation distance and carrier frequency, \(d_{0}\) represents the referenced distance which is typically set to 1 m, and \(n\) is the PLE of the CI model. \(X_{\lambda}^{\text{CI}}\) characterizes the shadow fading (SF), which follows a Gaussian distribution with a mean of zero and standard deviation of \(\lambda\)[30]. \(PL_{\text{FS}}(f,d_{0})\) represents the FSPL, which can be further expressed as  
\[PL_{\text{FS}}(f,d_{0})=20\text{log}_{10}(f)+20\text{log}_{10}(d_{0})+32.45 \tag{2}\]  
The 3 GPP model is considered to be a relatively authoritative PL model [31]. Taking the typical rural macro (RMa) scenario as an illustration, the 3 GPP model under LoS and NLoS paths can be, respectively, expressed as  
\[PL_{\text{3 GPP}}^{\text{LoS}}= \begin{cases}PL_{\text{3 GPP}}^{1},10\text{m} \leq d\leq d_{\text{P}}\\ PL_{\text{3 GPP}}^{2},d_{\text{P}}\leq d\leq 10\text{km}\end{cases}  \tag{3}\]  
\[PL_{\text{3 GPP}}^{\text{NLoS}}=\text{max}\Big{(}PL_{\text{3 GPP}}^{\text{LoS }},PL_{\text{3 GPP}}^{3}\Big{)} \tag{4}\]  
where \(PL_{\text{3 GPP}}^{1}\), \(PL_{\text{3 GPP}}^{2}\), and \(PL_{\text{3 GPP}}^{3}\) can be further expressed as  
\[PL_{\text{3 GPP}}^{1}=\text{log}_{10}(d)\times\text{min}\Big{(}10,0.03h_{ \text{B}}{}^{1.72}\Big{)}-\text{min}\Big{(}14.77,0.044h_{\text{B}}{}^{1.72} \Big{)} \tag{5}\]  
\[PL_{\text{3 GPP}}^{2}=PL_{\text{3 GPP}}^{1}(d_{\text{P}})+40\text{log}_{10}( d/d_{\text{P}}) \tag{6}\]\[ \begin{split} PL_{\text{3 GPP}}^{3}&=\left(\log_{10}(d)-3 \right)\times\left(43.42-3.1\text{log}_{10}(h_{\text{BS}})\right)-7.1\text{log} _{10}(w_{\text{S}})\\ &-\log_{10}(h_{\text{BS}})\times\left(24.37-3.7(h_{\text{B}}/h_{ \text{BS}})^{2}\right)+7.5\text{log}_{10}(h_{\text{B}})\\ &-\left(3.2\left(\text{log}_{10}(11.75h_{\text{UT}})\right)^{2}-4.97\right)+20\text{log}_{10}(f)+161.04\end{split}  \tag{7}\]  
where \(d_{\text{P}}\) represents the breakpoint distance, \(h_{\text{B}}\) represents the average height of buildings, \(w_{\text{S}}\) represents the average width of streets, and \(h_{\text{UT}}\) and \(h_{\text{BS}}\), respectively, represent the antenna heights of the user terminal and base station. Drawing inspiration from the principles of supervised learning in ML, we integrate traditional empirical models with ANN. By employing network architectures in place of explicit mathematical expressions, we aim to achieve more accurate predictions. Traditional NNs usually demand a substantial volume of measurement data during the training stage and it is difficult to acquire measurement data in scenarios like farmlands. Inspired by the NN's capability for second training, we designed a two-step training network named DWN-ANN. This network can conduct separate pre-training and optimized training by using RT simulation and measurement data to achieve a balance between prediction accuracy and the required amount of measurement data. Farmland scenarios are typically characterized by open spaces and limited scatters. Therefore, the A2G channel in the farmland scenario can be modeled as consisting of one LoS path, one ground specular NLoS path, and some scattering NLoS paths. This paper aims to achieve the PL prediction of the LoS and NLoS paths separately.