## 3 PL Prediction and DWN-ANN Design
### Network Structure  
The proposed DWN-ANN consists primarily of a pre-training module (PTM) and an optimization training module (OTM), as illustrated in Figure 2. The PTM can utilize RT simulation data for preliminary training to learn the approximate relationship between the PL and other channel parameters. This enables the network to attain an initial stable state, reducing the required amount of measurement data and accelerating the network's convergence rate. After pre-training, the network parameters of PTM are assigned to the OTM. The OTM can utilize a modest quantity of measurement data for fine-tuning, leading to the further optimization of the network parameters and enhancement of prediction accuracy. However, when conducting RT simulations in farmland scenarios, due to the limitation in the accuracy of reconstruction ground material, there might be significant disparities between the RT simulation data and the actual results. In such cases, relying solely on optimization with a small amount of measurement data might not be sufficient for error correction and achieving accurate PL prediction. Therefore, we also introduce an RT pre-correction module, utilizing the acquired measurement data to pre-correct parameters, such as the conductivity and relative dielectric constant of the ground material during RT simulations, so as to enhance the RT simulation accuracy. During the pre-training phase, the network's input is the RT simulation data. In this phase, only the weight and bias matrices of PTM within the DWN-ANN are updated, while the weight and bias matrices of OTM remain constant. The predicted results of PTM can be formulated as  
\[PI_{\text{PTM}}^{\text{LoS/NLoS}}=\text{F}_{\text{DWN-ANN}}\left(d_{\text{RT- Train}}^{\text{LoS/NLoS}},h_{\text{RT-Train}}^{\text{LoS/NLoS}},f_{\text{RT- Train}}^{\text{LoS/NLoS}}\Big{|}\mathbf{w}^{\text{PTM}},\mathbf{b}^{\text{ PTM}},\sigma(\cdot)\,\right) \tag{9}\]  
where \(d_{\text{RT-Train}}^{\text{LoS/NLoS}}\), \(h_{\text{RT-Train}}^{\text{LoS/NLoS}}\), and \(f_{\text{RT-Train}}^{\text{LoS/NLoS}}\) are the training set of propagation distance, UAV height, and carrier frequency obtained through the RT method, respectively. \(\mathbf{w}^{\text{PTM}}\) and \(\mathbf{b}^{\text{PTM}}\) are the weight and bias matrices of PTM, respectively. Figure 1: PL prediction scheme. After pre-training, the network proceeds to the optimization training phase, with the network's input changing to the training set of measurement data. At this point, the weight and bias matrices of PTM retain the pre-trained values and are assigned to the OTM. Subsequently, the network performs optimization training using the measurement data, fine-tuning the weight and bias matrices of OTM. The predicted results of OTM can be formulated as  
\[PL_{\text{OTM}}^{\text{LoS/NLoS}}=\text{F}_{\text{DWN-ANN}}\Big{(}d_{\text{ Meta-Train}}^{\text{LoS/NLoS}},h_{\text{Meta-Train}}^{\text{LoS/NLoS}},\text{ $\mathbf{\text{${\mathbf{w}}$}}^{\text{PTM}}$},\text{$\mathbf{\text{${\mathbf{w}}$}}^{\text{PTM }}$},\text{$\mathbf{\text{${\mathbf{w}}$}}^{\text{PTM}}$},\text{$\mathbf{\text{${\mathbf{w}}$}}^{ \text{OTM}}$},\text{$\mathbf{\text{${\mathbf{w}}$}}^{\text{OTM}}$},\sigma(\cdot)\Big{)} \tag{10}\]  
where \(d_{\text{Meta-Train}}^{\text{LoS/NLoS}}\), \(h_{\text{Meta-Train}}^{\text{LoS/NLoS}}\), and \(f_{\text{Meta-Train}}^{\text{LoS/NLoS}}\) are the training set of propagation distance, UAV height, and carrier frequency obtained through channel measurement, respectively. \(\text{$\mathbf{\text{${\mathbf{w}}$}}^{\text{OTM}}$}\) and \(\text{$\mathbf{\text{${\mathbf{w}}$}}^{\text{OTM}}$}\) are the weight and bias matrices of OTM, respectively.