Multiband Spectrum Sensing Based on the Sample Entropy
Cognitive radios represent a real alternative to the scarcity of the radio spectrum. One of the primary tasks of these radios is the detection of possible gaps in a given bandwidth used by licensed users (called also primary users). This task, called spectrum sensing, requires high precision in dete...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/3/411 |
| _version_ | 1827628982308175872 |
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| author | Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Rafael Aguilar-Gonzalez |
| author_facet | Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Rafael Aguilar-Gonzalez |
| author_sort | Yanqueleth Molina-Tenorio |
| collection | DOAJ |
| description | Cognitive radios represent a real alternative to the scarcity of the radio spectrum. One of the primary tasks of these radios is the detection of possible gaps in a given bandwidth used by licensed users (called also primary users). This task, called spectrum sensing, requires high precision in determining these gaps, maximizing the probability of detection. The design of spectrum sensing algorithms also requires innovative hardware and software solutions for real-time implementations. In this work, a technique to determine possible primary users’ transmissions in a wide frequency interval (multiband spectrum sensing) from the perspective of cognitive radios is presented. The proposal is implemented in a real wireless communications environment using low-cost hardware considering the sample entropy as a decision rule. To validate its feasibility for real-time implementation, a simulated scenario was first tested. Simulation and real-time implementations results were compared with the Higuchi fractal dimension as a decision rule. The encouraging results show that sample entropy correctly detects noise or a possible primary user transmission, with a probability of success around 0.99, and the number of samples with errors at the start and end of frequency edges of transmissions is, on average, only 12 samples. |
| first_indexed | 2024-03-09T13:43:19Z |
| format | Article |
| id | doaj.art-7a79f2f12b8c497bb061ddb99895db09 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T13:43:19Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7a79f2f12b8c497bb061ddb99895db092023-11-30T21:03:17ZengMDPI AGEntropy1099-43002022-03-0124341110.3390/e24030411Multiband Spectrum Sensing Based on the Sample EntropyYanqueleth Molina-Tenorio0Alfonso Prieto-Guerrero1Rafael Aguilar-Gonzalez2Information Science and Technology, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, MexicoElectrical Engineering Department, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, MexicoFaculty of Sciences, Autonomous University of San Luis Potosi, San Luis Potosi 78210, MexicoCognitive radios represent a real alternative to the scarcity of the radio spectrum. One of the primary tasks of these radios is the detection of possible gaps in a given bandwidth used by licensed users (called also primary users). This task, called spectrum sensing, requires high precision in determining these gaps, maximizing the probability of detection. The design of spectrum sensing algorithms also requires innovative hardware and software solutions for real-time implementations. In this work, a technique to determine possible primary users’ transmissions in a wide frequency interval (multiband spectrum sensing) from the perspective of cognitive radios is presented. The proposal is implemented in a real wireless communications environment using low-cost hardware considering the sample entropy as a decision rule. To validate its feasibility for real-time implementation, a simulated scenario was first tested. Simulation and real-time implementations results were compared with the Higuchi fractal dimension as a decision rule. The encouraging results show that sample entropy correctly detects noise or a possible primary user transmission, with a probability of success around 0.99, and the number of samples with errors at the start and end of frequency edges of transmissions is, on average, only 12 samples.https://www.mdpi.com/1099-4300/24/3/411cognitive radiosample entropysoftware-defined radiosmultiband spectrum sensingreal-time spectrum sensing |
| spellingShingle | Yanqueleth Molina-Tenorio Alfonso Prieto-Guerrero Rafael Aguilar-Gonzalez Multiband Spectrum Sensing Based on the Sample Entropy Entropy cognitive radio sample entropy software-defined radios multiband spectrum sensing real-time spectrum sensing |
| title | Multiband Spectrum Sensing Based on the Sample Entropy |
| title_full | Multiband Spectrum Sensing Based on the Sample Entropy |
| title_fullStr | Multiband Spectrum Sensing Based on the Sample Entropy |
| title_full_unstemmed | Multiband Spectrum Sensing Based on the Sample Entropy |
| title_short | Multiband Spectrum Sensing Based on the Sample Entropy |
| title_sort | multiband spectrum sensing based on the sample entropy |
| topic | cognitive radio sample entropy software-defined radios multiband spectrum sensing real-time spectrum sensing |
| url | https://www.mdpi.com/1099-4300/24/3/411 |
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