Big Data Analysis and Prediction of Electromagnetic Spectrum Resources: A Graph Approach

In the field of wireless communication, increasing number devices makes limited spectrum resources more scarce and accelerates complexity electromagnetic environment, posing a serious threat to sustainability industry’s development. Therefore, new effective technical methods are needed mine analyze activity rules reduce risk frequency conflict. This paper introduces idea graphs proposes resource analysis prediction architecture based on big data. this architecture, spatial correlation model activities is constructed through feature extraction. addition, model, depth learning network graph convolution designed, which uses prior information achieve efficient resources. Numerical experiments were carried out two datasets with different scales. Compared best baseline error reduced by 8.3% small-scale dataset 11.7% large-scale dataset. shows that proposed method applicable scales has obvious advantages in complex scenes large It can effectively use results domain improve accuracy