Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network
This work investigates spectrum sensing in cognitive radio networks, where multi-antenna secondary users aim to detect the spectral occupancy of noncircular signals transmitted by primary users. Specifically, we propose a deep-learning-based spectrum sensing approach using an augmented covariance-ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4791 |
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| author | Songlin Chen Zhenqing He Wenze Song Guohao Sun |
| author_facet | Songlin Chen Zhenqing He Wenze Song Guohao Sun |
| author_sort | Songlin Chen |
| collection | DOAJ |
| description | This work investigates spectrum sensing in cognitive radio networks, where multi-antenna secondary users aim to detect the spectral occupancy of noncircular signals transmitted by primary users. Specifically, we propose a deep-learning-based spectrum sensing approach using an augmented covariance-matrix-aware convolutional neural network (CNN). The core innovation of our approach lies in employing an augmented sample covariance matrix, which integrates both a standard covariance matrix and complementary covariance matrix, thereby fully exploiting the statistical properties of noncircular signals. By feeding augmented sample covariance matrices into the designed CNN architecture, the proposed approach effectively learns discriminative patterns from the underlying data structure, without stringent model constraints. Meanwhile, our approach eliminates the need for restrictive model assumptions and significantly enhances the detection performance by fully exploiting noncircular signal characteristics. Various experimental results demonstrate the significant performance improvement and generalization capability of the proposed approach compared to existing benchmark methods. |
| format | Article |
| id | doaj-art-a842b8f9909d4798bcaddc90f4dedba3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a842b8f9909d4798bcaddc90f4dedba32025-08-20T04:00:51ZengMDPI AGSensors1424-82202025-08-012515479110.3390/s25154791Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural NetworkSonglin Chen0Zhenqing He1Wenze Song2Guohao Sun3Southwest China Institute of Electronic Technology, Chengdu 610036, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, ChinaThis work investigates spectrum sensing in cognitive radio networks, where multi-antenna secondary users aim to detect the spectral occupancy of noncircular signals transmitted by primary users. Specifically, we propose a deep-learning-based spectrum sensing approach using an augmented covariance-matrix-aware convolutional neural network (CNN). The core innovation of our approach lies in employing an augmented sample covariance matrix, which integrates both a standard covariance matrix and complementary covariance matrix, thereby fully exploiting the statistical properties of noncircular signals. By feeding augmented sample covariance matrices into the designed CNN architecture, the proposed approach effectively learns discriminative patterns from the underlying data structure, without stringent model constraints. Meanwhile, our approach eliminates the need for restrictive model assumptions and significantly enhances the detection performance by fully exploiting noncircular signal characteristics. Various experimental results demonstrate the significant performance improvement and generalization capability of the proposed approach compared to existing benchmark methods.https://www.mdpi.com/1424-8220/25/15/4791cognitive radioconvolutional neural networkdeep learningnoncircular signalspectrum sensing |
| spellingShingle | Songlin Chen Zhenqing He Wenze Song Guohao Sun Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network Sensors cognitive radio convolutional neural network deep learning noncircular signal spectrum sensing |
| title | Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network |
| title_full | Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network |
| title_fullStr | Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network |
| title_full_unstemmed | Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network |
| title_short | Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network |
| title_sort | spectrum sensing for noncircular signals using augmented covariance matrix aware deep convolutional neural network |
| topic | cognitive radio convolutional neural network deep learning noncircular signal spectrum sensing |
| url | https://www.mdpi.com/1424-8220/25/15/4791 |
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