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

Abstract

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

A Novel Joint Time-Frequency Spectrum Resources Sustainable Risk Prediction Algorithm Based on TFBRL Network for the Electromagnetic Environment

“…[35] Model-enabled autoregressive network The model had both high frequency spectral predictability and fast model convergence speed. [36] Graph convolution network The model effectively reduced the spectrum prediction error of the multi-site.…”

“…The MTF 2 N constructed in [34] merged the CNN network and the LSTM network to achieve multi-channel and multi-slot spectrum prediction. To solve the problem of multiple training parameters and slow convergence of the machine learning model, the model-enabled autoregressive network [35] was proposed, and the model has both high frequency spectral predictability and fast model convergence speed.The authors of [36] constructed the electromagnetic spectrum graph neural network, which effectively reduced the spectrum prediction error of the multi-site.…”

Performance Analysis of a Self-Organized Network Dynamics Model for Public Opinion Information

“…At the same time, both sides of Equation ( 3) are simultaneously multiplied by 𝑙𝑝(𝑘, 𝑙) (〈𝑙〉𝑁 (𝑘,𝑙) (𝑡)) ⁄ to obtain Equation (14).…”

“…They underscored that network media information transmission network is self-organized, requiring four necessary conditions and certain driving forces for its realization. At the same time, scholars use self-organization charts to study the spread of disease [5][6][7][8][9][10][11][12][13][14] , being very similar to the spread of information. In summary, user selforganized network information dissemination is prevalent across various social platforms, and understanding selforganized network characteristics and user attributes is crucial for studying dissemination and guiding information governance.…”