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Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this res...

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Autores principales: Molina-Tenorio, Yanqueleth, Prieto-Guerrero, Alfonso, Aguilar-Gonzalez, Rafael, Lopez-Benitez, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256076/
https://www.ncbi.nlm.nih.gov/pubmed/37299936
http://dx.doi.org/10.3390/s23115209
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author Molina-Tenorio, Yanqueleth
Prieto-Guerrero, Alfonso
Aguilar-Gonzalez, Rafael
Lopez-Benitez, Miguel
author_facet Molina-Tenorio, Yanqueleth
Prieto-Guerrero, Alfonso
Aguilar-Gonzalez, Rafael
Lopez-Benitez, Miguel
author_sort Molina-Tenorio, Yanqueleth
collection PubMed
description Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.
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spelling pubmed-102560762023-06-10 Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks Molina-Tenorio, Yanqueleth Prieto-Guerrero, Alfonso Aguilar-Gonzalez, Rafael Lopez-Benitez, Miguel Sensors (Basel) Article Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth. MDPI 2023-05-30 /pmc/articles/PMC10256076/ /pubmed/37299936 http://dx.doi.org/10.3390/s23115209 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Molina-Tenorio, Yanqueleth
Prieto-Guerrero, Alfonso
Aguilar-Gonzalez, Rafael
Lopez-Benitez, Miguel
Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
title Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
title_full Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
title_fullStr Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
title_full_unstemmed Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
title_short Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
title_sort cooperative multiband spectrum sensing using radio environment maps and neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256076/
https://www.ncbi.nlm.nih.gov/pubmed/37299936
http://dx.doi.org/10.3390/s23115209
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