Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications
In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7907 |
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| author | Sara E. Abdelbaset Hossam M. Kasem Ashraf A. Khalaf Amr H. Hussein Ahmed A. Kabeel |
| author_facet | Sara E. Abdelbaset Hossam M. Kasem Ashraf A. Khalaf Amr H. Hussein Ahmed A. Kabeel |
| author_sort | Sara E. Abdelbaset |
| collection | DOAJ |
| description | In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing. Our research introduces a groundbreaking approach to spectrum sensing by leveraging convolutional neural networks (CNNs) to significantly advance the precision and effectiveness of identifying unused frequency bands. We treat spectrum sensing as a classification task and train our model with diverse signal types and noise data, enabling unparalleled adaptability to novel signals. Our method surpasses traditional techniques such as the maximum–minimum eigenvalue ratio-based and frequency domain entropy-based methods, showcasing superior performance and adaptability. In particular, our CNN-based approach demonstrates exceptional accuracy, even outperforming established methods when faced with additive white Gaussian noise (AWGN). |
| format | Article |
| id | doaj-art-ce093fd5db56412b9b947e9dbb3fb426 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ce093fd5db56412b9b947e9dbb3fb4262024-12-27T14:52:24ZengMDPI AGSensors1424-82202024-12-012424790710.3390/s24247907Deep Learning-Based Spectrum Sensing for Cognitive Radio ApplicationsSara E. Abdelbaset0Hossam M. Kasem1Ashraf A. Khalaf2Amr H. Hussein3Ahmed A. Kabeel4Electronics and Electrical Communications Engineering Department, Higher Institute of Engineering and Technology, New Damietta 34517, EgyptElectronics and Communications Department, Faculty of Engineering, Tanta University, Tanta 31511, EgyptElectronics and Electrical Communication Department, Faculty of Engineering, AL Menya University, Minia 61519, EgyptElectronics and Communications Department, Faculty of Engineering, Tanta University, Tanta 31511, EgyptElectronics and Communication Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa 35511, EgyptIn order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing. Our research introduces a groundbreaking approach to spectrum sensing by leveraging convolutional neural networks (CNNs) to significantly advance the precision and effectiveness of identifying unused frequency bands. We treat spectrum sensing as a classification task and train our model with diverse signal types and noise data, enabling unparalleled adaptability to novel signals. Our method surpasses traditional techniques such as the maximum–minimum eigenvalue ratio-based and frequency domain entropy-based methods, showcasing superior performance and adaptability. In particular, our CNN-based approach demonstrates exceptional accuracy, even outperforming established methods when faced with additive white Gaussian noise (AWGN).https://www.mdpi.com/1424-8220/24/24/7907convolutional neural networksdeep learningspectrum sensingcognitive radio |
| spellingShingle | Sara E. Abdelbaset Hossam M. Kasem Ashraf A. Khalaf Amr H. Hussein Ahmed A. Kabeel Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications Sensors convolutional neural networks deep learning spectrum sensing cognitive radio |
| title | Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications |
| title_full | Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications |
| title_fullStr | Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications |
| title_full_unstemmed | Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications |
| title_short | Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications |
| title_sort | deep learning based spectrum sensing for cognitive radio applications |
| topic | convolutional neural networks deep learning spectrum sensing cognitive radio |
| url | https://www.mdpi.com/1424-8220/24/24/7907 |
| work_keys_str_mv | AT saraeabdelbaset deeplearningbasedspectrumsensingforcognitiveradioapplications AT hossammkasem deeplearningbasedspectrumsensingforcognitiveradioapplications AT ashrafakhalaf deeplearningbasedspectrumsensingforcognitiveradioapplications AT amrhhussein deeplearningbasedspectrumsensingforcognitiveradioapplications AT ahmedakabeel deeplearningbasedspectrumsensingforcognitiveradioapplications |