Research on multi-dimensional data-based 5G base station electromagnetic radiation intensity prediction model using improved graph neural network

The rapid rollout of 5G networks has heightened the necessity for accurate forecasting of electromagnetic radiation (EMR) intensity emanating from 5G base stations. In this paper, we introduce a cutting-edge Enhanced Graph Neural Network (GNN) model specifically designed for predicting EMR intensity. This model tackles the intricate spatial and network interactions inherent to 5G infrastructure. By harnessing the capabilities of Graph Neural Networks in analyzing multifaceted data, our approach marks a substantial advancement over traditional predictive frameworks. Comprehensive empirical tests showcase the model's remarkable precision in EMR intensity prediction when pitted against standard machine learning practices and a baseline neural network. This paper not only facilitates the safe rollout and governance of 5G networks but also underscores the utilization of sophisticated machine learning paradigms in the telecommunications realm. The revelations carry profound ramifications for public health and safety, regulatory adherence, and the subsequent evolution of wireless communication networks.