SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks
Spectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2022/3328734 |
| _version_ | 1797972520939290624 |
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| author | M. Suriya M. G. Sumithra |
| author_facet | M. Suriya M. G. Sumithra |
| author_sort | M. Suriya |
| collection | DOAJ |
| description | Spectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of secondary user (SU) signals. For signal classification, existing methods, including cooperative, noncooperative, and neural network-based models, are frequently used, but they are still inconsistent because they lack sensitivity and accuracy. A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. It can quickly and accurately monitor the spectrum from spectrogram images by utilizing cyclostationary features and convolutional neural networks (CNN). The class imbalance issue is solved by uniformly spreading the samples throughout the classes using the oversampling method known as SMOTE (Synthetic Minority Oversampling Technique). The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters. |
| first_indexed | 2024-04-11T03:49:47Z |
| format | Article |
| id | doaj.art-80e95f48c406480cab1f942904daca7e |
| institution | Directory Open Access Journal |
| issn | 1687-5877 |
| language | English |
| last_indexed | 2024-04-11T03:49:47Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj.art-80e95f48c406480cab1f942904daca7e2023-01-02T02:13:20ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58772022-01-01202210.1155/2022/3328734SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural NetworksM. Suriya0M. G. Sumithra1Sri Eshwar College of EngineeringDr.N.G.P. Institute of TechnologySpectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of secondary user (SU) signals. For signal classification, existing methods, including cooperative, noncooperative, and neural network-based models, are frequently used, but they are still inconsistent because they lack sensitivity and accuracy. A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. It can quickly and accurately monitor the spectrum from spectrogram images by utilizing cyclostationary features and convolutional neural networks (CNN). The class imbalance issue is solved by uniformly spreading the samples throughout the classes using the oversampling method known as SMOTE (Synthetic Minority Oversampling Technique). The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters.http://dx.doi.org/10.1155/2022/3328734 |
| spellingShingle | M. Suriya M. G. Sumithra SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks International Journal of Antennas and Propagation |
| title | SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks |
| title_full | SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks |
| title_fullStr | SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks |
| title_full_unstemmed | SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks |
| title_short | SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks |
| title_sort | spectrumnet cooperative spectrum monitoring using deep neural networks |
| url | http://dx.doi.org/10.1155/2022/3328734 |
| work_keys_str_mv | AT msuriya spectrumnetcooperativespectrummonitoringusingdeepneuralnetworks AT mgsumithra spectrumnetcooperativespectrummonitoringusingdeepneuralnetworks |