Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach
The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learnin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4183 |
| _version_ | 1797575937228800000 |
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| author | Oguz Bedir Ali Riza Ekti Mehmet Kemal Ozdemir |
| author_facet | Oguz Bedir Ali Riza Ekti Mehmet Kemal Ozdemir |
| author_sort | Oguz Bedir |
| collection | DOAJ |
| description | The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection. |
| first_indexed | 2024-03-10T21:45:24Z |
| format | Article |
| id | doaj.art-1789eba804634dafbb03fee774a3e16c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:45:24Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1789eba804634dafbb03fee774a3e16c2023-11-19T14:18:24ZengMDPI AGElectronics2079-92922023-10-011219418310.3390/electronics12194183Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage ApproachOguz Bedir0Ali Riza Ekti1Mehmet Kemal Ozdemir2Graduate School of Engineering and Natural Sciences, Istanbul Medipol University, 34810 Istanbul, TurkeyDepartment of Electrical-Electronics Engineering, Balikesir University, 10145 Balikesir, TurkeyDepartment of Computer Engineering, Istanbul Medipol University, 34810 Istanbul, TurkeyThe concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.https://www.mdpi.com/2079-9292/12/19/4183spectrum sensingenergy detectiondeep learningadaptive threshold |
| spellingShingle | Oguz Bedir Ali Riza Ekti Mehmet Kemal Ozdemir Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach Electronics spectrum sensing energy detection deep learning adaptive threshold |
| title | Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach |
| title_full | Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach |
| title_fullStr | Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach |
| title_full_unstemmed | Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach |
| title_short | Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach |
| title_sort | exploring deep learning for adaptive energy detection threshold determination a multistage approach |
| topic | spectrum sensing energy detection deep learning adaptive threshold |
| url | https://www.mdpi.com/2079-9292/12/19/4183 |
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