Prediction-Based Spectrum Sensing Framework for Cognitive Radio

This paper presents a hardware-software deep learning architecture for prediction-based spectrum sensing in Cognitive Radio (CR) applications. A convolutional neural network-based predictor for spectrum occupancy was trained using the band power from I/Q samples acquired by a softwaredefined radio (...

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Detalles Bibliográficos
Autores: Rojas, Andrés, Follet, Gawen, Jovanovic-Dolecek, G., Rosa, José M. de la, Liñán-Cembrano, Gustavo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/405691
Acceso en línea:http://hdl.handle.net/10261/405691
https://api.elsevier.com/content/abstract/scopus_id/105014471167
Access Level:acceso abierto
Palabra clave:Cognitive radio
Deep learning
Software defined radio
Spectrogram
Spectrum occupancy prediction
Spectrum sensing
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spelling Prediction-Based Spectrum Sensing Framework for Cognitive RadioRojas, AndrésFollet, GawenJovanovic-Dolecek, G.Rosa, José M. de laLiñán-Cembrano, GustavoCognitive radioDeep learningSoftware defined radioSpectrogramSpectrum occupancy predictionSpectrum sensingThis paper presents a hardware-software deep learning architecture for prediction-based spectrum sensing in Cognitive Radio (CR) applications. A convolutional neural network-based predictor for spectrum occupancy was trained using the band power from I/Q samples acquired by a softwaredefined radio (SDR). Additionally, a second neural engine was trained for radio frequency (RF) frame detection based on spectrograms. We implemented a transfer-learning solution using a You-Only-LookOnce version 8 nano model with a synthetic dataset comprising thousands of wireless signals, including Wi-Fi, Bluetooth, and collision frames. Once trained, the two neural networks were transferred to a Raspberry Pi 5 - an affordable single-board computer - connected to two (one for Rx, one for Tx) ADALM-PLUTO SDR systems for benchmarking using over-the-air signals in the 2.4 GHz band. Together with our methodology and experimental results, the paper also presents an overview of current spectrum prediction proposals and RF frame detectors. Remarkably, to the best of our knowledge, this proposed framework is the first approach towards an Internet of Things-suitable implementation of prediction-based spectrum sensing for CR applications.This publication has been funded by grant USECHIP (TSI-069100-2023-001), project funded by the Secretary of State for Telecommunications and Digital Infrastructure, Ministry for the Digital Transformation and Civil Service and by the European Union Next Generation EU/PRTR. This work was also supported in part by Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti) and by Grants PID2022-138078OB-I00, and PDC2023-145808-I00, funded by MICIU/AEI/10.13039/501100011033 and by the European Union, ERDF A way of making Europe.Peer reviewedInstitute of Electrical and Electronics EngineersMinisterio para la Transformación Digital y de la Función Pública (España)Agencia Estatal de Investigación (España)European CommissionMinisterio de Ciencia, Innovación y Universidades (España)Rojas, Andrés [0000-0003-1773-8514]Jovanovic-Dolecek, G. [0000-0003-1258-5176]Rosa, José M. de la [0000-0003-2848-9226]Liñán-Cembrano, Gustavo [0000-0003-1839-555X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/405691https://api.elsevier.com/content/abstract/scopus_id/105014471167reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI//TSI-069100-2023-001info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-138078OB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2023-145808-I00https://doi.org/10.1109/OJCAS.2025.3592376Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4056912026-05-22T06:33:51Z
dc.title.none.fl_str_mv Prediction-Based Spectrum Sensing Framework for Cognitive Radio
title Prediction-Based Spectrum Sensing Framework for Cognitive Radio
spellingShingle Prediction-Based Spectrum Sensing Framework for Cognitive Radio
Rojas, Andrés
Cognitive radio
Deep learning
Software defined radio
Spectrogram
Spectrum occupancy prediction
Spectrum sensing
title_short Prediction-Based Spectrum Sensing Framework for Cognitive Radio
title_full Prediction-Based Spectrum Sensing Framework for Cognitive Radio
title_fullStr Prediction-Based Spectrum Sensing Framework for Cognitive Radio
title_full_unstemmed Prediction-Based Spectrum Sensing Framework for Cognitive Radio
title_sort Prediction-Based Spectrum Sensing Framework for Cognitive Radio
dc.creator.none.fl_str_mv Rojas, Andrés
Follet, Gawen
Jovanovic-Dolecek, G.
Rosa, José M. de la
Liñán-Cembrano, Gustavo
author Rojas, Andrés
author_facet Rojas, Andrés
Follet, Gawen
Jovanovic-Dolecek, G.
Rosa, José M. de la
Liñán-Cembrano, Gustavo
author_role author
author2 Follet, Gawen
Jovanovic-Dolecek, G.
Rosa, José M. de la
Liñán-Cembrano, Gustavo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio para la Transformación Digital y de la Función Pública (España)
Agencia Estatal de Investigación (España)
European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Rojas, Andrés [0000-0003-1773-8514]
Jovanovic-Dolecek, G. [0000-0003-1258-5176]
Rosa, José M. de la [0000-0003-2848-9226]
Liñán-Cembrano, Gustavo [0000-0003-1839-555X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Cognitive radio
Deep learning
Software defined radio
Spectrogram
Spectrum occupancy prediction
Spectrum sensing
topic Cognitive radio
Deep learning
Software defined radio
Spectrogram
Spectrum occupancy prediction
Spectrum sensing
description This paper presents a hardware-software deep learning architecture for prediction-based spectrum sensing in Cognitive Radio (CR) applications. A convolutional neural network-based predictor for spectrum occupancy was trained using the band power from I/Q samples acquired by a softwaredefined radio (SDR). Additionally, a second neural engine was trained for radio frequency (RF) frame detection based on spectrograms. We implemented a transfer-learning solution using a You-Only-LookOnce version 8 nano model with a synthetic dataset comprising thousands of wireless signals, including Wi-Fi, Bluetooth, and collision frames. Once trained, the two neural networks were transferred to a Raspberry Pi 5 - an affordable single-board computer - connected to two (one for Rx, one for Tx) ADALM-PLUTO SDR systems for benchmarking using over-the-air signals in the 2.4 GHz band. Together with our methodology and experimental results, the paper also presents an overview of current spectrum prediction proposals and RF frame detectors. Remarkably, to the best of our knowledge, this proposed framework is the first approach towards an Internet of Things-suitable implementation of prediction-based spectrum sensing for CR applications.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/405691
https://api.elsevier.com/content/abstract/scopus_id/105014471167
url http://hdl.handle.net/10261/405691
https://api.elsevier.com/content/abstract/scopus_id/105014471167
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI//TSI-069100-2023-001
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-138078OB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2023-145808-I00
https://doi.org/10.1109/OJCAS.2025.3592376
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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repository.mail.fl_str_mv
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