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

Han Zhang, Siqi Peng, Jingyu Zhang and Yun Lin ()
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Han Zhang: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Siqi Peng: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Jingyu Zhang: School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410004, China
Yun Lin: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

Sustainability, 2022, vol. 15, issue 1, 1-17

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.

Keywords: spectrum resources; big data analysis and prediction; graph; spatial correlation; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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