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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/5209 |
| _version_ | 1797596709266653184 |
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| author | Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Rafael Aguilar-Gonzalez Miguel Lopez-Benitez |
| author_facet | Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Rafael Aguilar-Gonzalez Miguel Lopez-Benitez |
| author_sort | Yanqueleth Molina-Tenorio |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T02:56:52Z |
| format | Article |
| id | doaj.art-9b8dd7b31fac4022a7cdbbf2d3d73eb1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:56:52Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9b8dd7b31fac4022a7cdbbf2d3d73eb12023-11-18T08:34:02ZengMDPI AGSensors1424-82202023-05-012311520910.3390/s23115209Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural NetworksYanqueleth Molina-Tenorio0Alfonso Prieto-Guerrero1Rafael Aguilar-Gonzalez2Miguel Lopez-Benitez3Information Science and Technology Ph.D., Metropolitan Autonomous University, Mexico City 09360, MexicoElectrical Engineering Department, Metropolitan Autonomous University, Mexico City 09360, MexicoFaculty of Science, Autonomous University of San Luis Potosi, San Luis Potosi 78210, MexicoDepartment of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UKCogitive 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.https://www.mdpi.com/1424-8220/23/11/5209multiband spectrum sensingcognitive radiosradio environment mapsneural networkscooperative sensor networksreal-time implementation |
| spellingShingle | Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Rafael Aguilar-Gonzalez Miguel Lopez-Benitez Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks Sensors multiband spectrum sensing cognitive radios radio environment maps neural networks cooperative sensor networks real-time implementation |
| 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 | multiband spectrum sensing cognitive radios radio environment maps neural networks cooperative sensor networks real-time implementation |
| url | https://www.mdpi.com/1424-8220/23/11/5209 |
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