In order to improve the prediction accuracy and robustness of radio wave propagation in the very high-frequency (VHF) band, we proposed a combination prediction model (hereinafter referred to as CPM) based on the entropy weight theory and the machine learning method. The improved entropy weight method is used to determine the initial weights of recommendation ITU-R P.1546 and ITU-R P.2001 in the combination prediction model based on the systematic analysis of the prediction performance of the two ITU-R models. Furtherly, we modeled the weight mapping using the machine learning method. The statistical comparison shows that the prediction accuracy and robustness of the proposed model are better than ITU-R P.1546 and P.2001 models, in which the root mean square error (RMSE) of path loss is reduced by 3.56dB and 7.35dB, respectively. In addition, the relative error (RE) of path loss is reduced by 0.0157 dB and 0.0255 dB, respectively, corresponding to a 40.53% and 52.62% reduction in the RE of path loss. The above research can provide basic support for the regional assimilation of radio propagation prediction methods.

The radio channel model is a major factor supporting the whole life cycle of the terrestrial radio system, including the demonstration, design, validation, operation, and so on. To improve the spectrum sharing and spectral efficiency in terrestrial radio services, we analyze three types of path loss models in detail: deterministic, empirical, and semi-empirical models, to meet the requirements of path loss modeling for supporting traditional band expansion and reuse. Then, we conduct a comparative analysis based on the characteristics of the current models. Furthermore, a preview of the future terrestrial path loss modeling methods is provided, including intelligent modeling processes and multi-model hybridization methods. Finally, we look forward to the potential technology that can be used in future wireless communication, such as terahertz communication, reconfigurable intelligent surface technology, and integrated communication and sensing technology. The above research can provide a reference for the development of terrestrial radio channel modeling, promoting the technologies of terrestrial channel modeling. We hope this paper will stimulate more interest in modeling terrestrial radio channels.

For improving the accuracy and robustness of predicting radio propagation in the frequency bands of FM Broadcasting Services, we proposed a propagation prediction model suitable for multi-band and multi-scenario based on Support Vector Regression (SVR). The modeling is based on Beijing’s measurement data, covering various environments, such as rural, suburban, and urban areas, with a frequency range of 86.7MHz-106.6MHz. A comparison will be made to verify the model’s performance with the ITU-R P.1546 model, an internationally common model recommended by ITU. The results show that the model exhibits superior prediction accuracy and stability compared to ITU-R P.1546, and the average relative error of ITU-R P.1546 model prediction in each frequency band is prominent, from 3.01% to 4.77%. Moreover, the average relative error of each frequency band of the model is distributed by about 2%-2.5%. In addition, SVR’s the predicted direction and details are highly similar to ITU-R P.1546, which can better fit the trend of propagation loss with propagation distance and other conditions. As a result, the research can provide a reference for regional application and localization research of radio propagation prediction methods.

The rapid development of terrestrial broadcasting services has heightened the demands for terrestrial broadcasting coverage capabilities and system planning. This paper proposed an integrated prediction method suitable for multi-band and multi-scene applications to construct high-quality Radio Maps in Frequency Modulation (FM) bands, defined as FM Broadcasting Maps. The method is based on Particle Swarm Optimization (PSO) assisted Support Vector Machines (SVM) and takes Beijing and its surrounding areas as an example for practice. FM Broadcasting Maps are constructed based on three typical propagation prediction models and measurements collected in Beijing and its surrounding areas. The measurements encompass 11 frequencies ranging from 86.7MHz to 106.6MHz, encompassing various reception scenarios like rural and urban areas. The proposed method considers factors such as the receiving location, terrain, frequency, and other relevant information specific to the Beijing area. It matches different areas with suitable propagation models to create the FM Broadcasting Maps, combining the strengths of the three models. To verify the proposed method, we employ the relative error as the evaluation criterion to evaluate the prediction performance of the proposed and the typical model. The comparison demonstrates the significant advantages of the proposed method in terms of prediction accuracy and stability. This research is a valuable reference for regional application and localization research of radio wave propagation prediction methods in the FM broadcasting band. It offers valuable insights for analyzing broadcast signal quality levels and provides a priori reference for specific application scenarios.

Frequency modulation (FM) broadcasting is a robust and widely applied technology that offers unparalleled advantages over other broadcasting methods in challenging environments. In order to achieve high accuracy in constructing broadcasting maps for scenarios with uneven and sparse distribution of measurement data, we introduce the concept of FM broadcasting maps and propose a novel methodology for their construction. This paper utilizes the Long Short-Term Memory (LSTM) model to assimilate predictions from the ITU-R models for modeling purposes. To begin, we analyzed critical environmental parameters influencing radio wave propagation. Based on this analysis, we identified the foundational input features for the LSTM model. Subsequently, predictions from the ITU-R P.1546 and 2001 models were assimilated as features and input into the LSTM model for training, resulting in assimilation modeling. Finally, a broadcast map is constructed using the parameter construction method based on the proposed model. The results indicate that the relative error between the measurements and the proposed models, ITU-R P.1546 and ITU-R P.2001, are 3.14%, 6.48%, and 9.89%, respectively. The prediction accuracy of the proposed model surpasses that of the ITU-R models, and stability is significantly improved compared to models solely based on LSTM. The broadcast map in this paper provides an objective reflection of measured field strength values across multiple dimensions, including frequency, distance, various terrains, and error distribution. It demonstrates notable advantages in scenarios characterized by sparse and unevenly distributed sampling points.

In this study, we aim to analyze the electromagnetic interference (EMI) regarding the Five-hundred-meter Aperture Spherical radio Telescope (FAST) caused by base stations in the International Mobile Telecommunications-2000 (IMT-2000) frequency band. By analyzing the frequency bands used by the transmitting and receiving devices and the surrounding environmental parameters and utilizing an approach to predicting radio wave propagation loss that is based on deterministic methods, we conclude by comparing the predicted received power at the FAST with its interference protection threshold. Our analysis demonstrates that, currently, only 55.31% of IMT-2000 base stations in the FAST radio quiet zone (RQZ) meet the protection threshold. Additionally, this article verifies the applicability and accuracy of the radio wave propagation model used in the research based on field strength measurements. Overall, this study provides valuable insights for improving the electromagnetic environment surrounding FAST and reducing the EMI caused by mobile communication base stations. It also provides corresponding analysis methods and useful suggestions for analyzing electromagnetic radiation interference in other radio telescopes.