Broadcasting Map Construction Method Based on Particle Swarm Optimization-Assisted Support Vector Machine Integrated Model

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Abstract
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.
Funding Information
  • National Natural Science Foundation of China (62031008)

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