### Proposed Solution and Contributions  
In conclusion, existing studies provide valuable insights into path loss prediction and channel modeling but have limitations in areas such as optimizing neural network configurations, determining the number of networks in ensemble models, and allocating output weights. Traditional models, like ray-tracing and geometry-based stochastic models, are inefficient and costly in high-speed train environments, limiting their accuracy and generalization. While advanced neural network methods like CNN and RNN show potential, they struggle with the complex channel characteristics in high-speed train carriages. CNNs are good at feature extraction but unstable in dynamic environments, while RNNs are strong at capturing temporal features but have limited generalization in noisy conditions. Moreover, ensemble learning methods still face challenges, such as choosing the optimal number of networks and efficiently allocating output weights, limiting their performance in path loss prediction.  
To overcome these challenges, the research presented in this paper proposes a multi-neural network ensemble approach for path loss prediction in high-speed train carriages. Optimizing output weight configurations is carried out to improve accuracy and stability. Performance comparisons with traditional and advanced neural network methods will demonstrate the advantages of our approach. The primary contributions of this paper are as follows:  
* A channel measurement campaign at 2.6 GHz was conducted in a high-speed train environment. Based on the measurement data, an RT model was calibrated. The calibrated RT model can generate large-scale, authentic, and reliable channel datasets.
* The performance of neural networks with varying numbers of hidden layer neurons was evaluated to address the complexity of channel modeling in high-speed train compartments. A candidate set of neural networks was constructed, and progressive integration methods were applied to optimize the overall prediction performance. Subsequently, progressive screening was employed to identify the optimal combination of neural networks for optimizing the overall prediction.
* To tackle the challenge of optimizing the output weights of base learners in ensemble learning, the whale optimization algorithm was introduced. By adaptively adjustingthe output weights of each base learner, the prediction accuracy and robustness of the ensemble model were significantly improved.
* The experimental results show that, compared with other methods, the proposed model achieves substantial improvements in root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination \(\mathbb{R}^{2}\), thereby validating its effectiveness and practicality.  
The following section outlines the structure of the remainder of this paper. Section 2 presents the communication scenario of high-speed rail signals penetrating train carriages and outlines constructing a high-precision dataset. Section 3 elaborates on the overall framework of the field strength prediction method for high-speed train carriages and the multi-neural network ensemble approach. Section 4 evaluates and compares the performance of the multi-neural network ensemble model with traditional and advanced neural network methods using the prediction dataset. Section 5 successfully summarizes the study's findings and outlines future work.