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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Frequency spectrum allocation has been a subject of dispute in recent years. Cognitive radio dynamically allocates users to spectrum holes using various sensing techniques. Noise levels and distances between users can significantly impact the efficiency of cognitive radio systems. Designing robust communication systems requires accurate knowledge of these factors. This paper proposes a method for predicting noise levels and distances based on spectrum sensing signals using regression machine learning models. The proposed methods achieved correlation coefficients of over 0.98 and 0.82 for noise and distance predictions, respectively. Accurately estimating these parameters enables adaptive resource allocation, interference mitigation, and improved spectrum efficiency, ultimately enhancing the performance and reliability of cognitive radio networks.

Details

Title
Predicting Noise and User Distances from Spectrum Sensing Signals Using Transformer and Regression Models
Author
Valadão Myke 1   VIAFID ORCID Logo  ; Amoedo Diego 2   VIAFID ORCID Logo  ; Costa, André 3   VIAFID ORCID Logo  ; Carvalho Celso 4   VIAFID ORCID Logo  ; Sabino Waldir 4   VIAFID ORCID Logo 

 Center for R&D in Electronic and Information Technology (CETELI), Department of Electronics and Computing (DTEC), Federal University of Amazonas (UFAM), Manaus 69067005, Brazil; [email protected] (D.A.); [email protected] (C.C.); [email protected] (W.S.), Sidi—Institute for Research, Development, and Innovation, Manaus 69037000, Brazil 
 Center for R&D in Electronic and Information Technology (CETELI), Department of Electronics and Computing (DTEC), Federal University of Amazonas (UFAM), Manaus 69067005, Brazil; [email protected] (D.A.); [email protected] (C.C.); [email protected] (W.S.), National Telecommunications Agency (ANATEL), Manaus 69057070, Brazil 
 Electronic and Telecommunications Engineering, Faculty of Electrical Engineering (FEELT), Federal University of Uberlândia (UFU), Uberlândia 38408288, Brazil; [email protected] 
 Center for R&D in Electronic and Information Technology (CETELI), Department of Electronics and Computing (DTEC), Federal University of Amazonas (UFAM), Manaus 69067005, Brazil; [email protected] (D.A.); [email protected] (C.C.); [email protected] (W.S.) 
First page
4296
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3194489682
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.