Cyclostationary and energy detection spectrum sensing beyond 5G waveforms
The cyclostationary spectrum (CS) method is one of the best at what it does because it effectively detects idle spectrum with low signal-to-noise ratios (SNR). In order to distinguish the signal in a noisy environment, gather more data that aids in a better analysis of signals, and use spectral corr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-04-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023172?viewType=HTML |
| _version_ | 1797808497019060224 |
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| author | Arun Kumar J Venkatesh Nishant Gaur Mohammed H. Alsharif Peerapong Uthansakul Monthippa Uthansakul |
| author_facet | Arun Kumar J Venkatesh Nishant Gaur Mohammed H. Alsharif Peerapong Uthansakul Monthippa Uthansakul |
| author_sort | Arun Kumar |
| collection | DOAJ |
| description | The cyclostationary spectrum (CS) method is one of the best at what it does because it effectively detects idle spectrum with low signal-to-noise ratios (SNR). In order to distinguish the signal in a noisy environment, gather more data that aids in a better analysis of signals, and use spectral correlation for dependable framework modelling, CS achieves optimal performance characteristics. High intricacy is seen as one of the CS's shortcomings. In this article, we suggest a novel CS algorithm for 5G waveforms. By restricting the computation of cyclostationary characteristics and the signal autocorrelation, the complexity of CS is reduced. To evaluate the performance of 5G waveforms, the Energy Detection (ED) and CS spectrum sensing algorithms based on cognitive radio (CR) are presented. The results of the study show that the suggested CS algorithm did a good job of detection and gained 2 dB compared to the conventional standards. |
| first_indexed | 2024-03-13T06:38:24Z |
| format | Article |
| id | doaj.art-b3fa15d7df56483193ff165a96426826 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-13T06:38:24Z |
| publishDate | 2023-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-b3fa15d7df56483193ff165a964268262023-06-09T01:08:51ZengAIMS PressElectronic Research Archive2688-15942023-04-013163400341610.3934/era.2023172Cyclostationary and energy detection spectrum sensing beyond 5G waveformsArun Kumar0J Venkatesh1Nishant Gaur2Mohammed H. Alsharif3Peerapong Uthansakul4Monthippa Uthansakul51. Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India2. Department of CSE, Chennai Institute of Technology, Chennai, India3. Department of Physics, JECRC University, Jaipur, India4. Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea5. School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand5. School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandThe cyclostationary spectrum (CS) method is one of the best at what it does because it effectively detects idle spectrum with low signal-to-noise ratios (SNR). In order to distinguish the signal in a noisy environment, gather more data that aids in a better analysis of signals, and use spectral correlation for dependable framework modelling, CS achieves optimal performance characteristics. High intricacy is seen as one of the CS's shortcomings. In this article, we suggest a novel CS algorithm for 5G waveforms. By restricting the computation of cyclostationary characteristics and the signal autocorrelation, the complexity of CS is reduced. To evaluate the performance of 5G waveforms, the Energy Detection (ED) and CS spectrum sensing algorithms based on cognitive radio (CR) are presented. The results of the study show that the suggested CS algorithm did a good job of detection and gained 2 dB compared to the conventional standards.https://www.aimspress.com/article/doi/10.3934/era.2023172?viewType=HTMLcyclostationaryfifth generation5g6gcognitive radiocrspectrum sensing |
| spellingShingle | Arun Kumar J Venkatesh Nishant Gaur Mohammed H. Alsharif Peerapong Uthansakul Monthippa Uthansakul Cyclostationary and energy detection spectrum sensing beyond 5G waveforms Electronic Research Archive cyclostationary fifth generation 5g 6g cognitive radio cr spectrum sensing |
| title | Cyclostationary and energy detection spectrum sensing beyond 5G waveforms |
| title_full | Cyclostationary and energy detection spectrum sensing beyond 5G waveforms |
| title_fullStr | Cyclostationary and energy detection spectrum sensing beyond 5G waveforms |
| title_full_unstemmed | Cyclostationary and energy detection spectrum sensing beyond 5G waveforms |
| title_short | Cyclostationary and energy detection spectrum sensing beyond 5G waveforms |
| title_sort | cyclostationary and energy detection spectrum sensing beyond 5g waveforms |
| topic | cyclostationary fifth generation 5g 6g cognitive radio cr spectrum sensing |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023172?viewType=HTML |
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