Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in order to solve this problem, and a spectrum sensing...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/6/1317 |
| _version_ | 1797612199155335168 |
|---|---|
| author | Xinyu Li Zhijin Zhao Yupei Zhang Shilian Zheng Shaogang Dai |
| author_facet | Xinyu Li Zhijin Zhao Yupei Zhang Shilian Zheng Shaogang Dai |
| author_sort | Xinyu Li |
| collection | DOAJ |
| description | The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in order to solve this problem, and a spectrum sensing algorithm based on self-supervised contrast learning (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>S</mi><mi>C</mi><mi>L</mi></mrow></semantics></math></inline-formula>) is proposed. The algorithm mainly includes two stages: pre-training and fine-tuning. In the pre-training stage, according to the characteristics of communication signals, data augmentation methods are designed to obtain the pre-trained positive sample pairs, and the features of the positive sample pairs of unlabeled samples are extracted by self-supervised contrast learning to obtain the feature extractor. In the fine-tuning stage, the parameters of the feature extraction layer are frozen, and a small number of labeled samples are used to update the parameters of the classification layer, and the features and labels are connected to get the spectrum sensing classifier. The simulation results demonstrate that the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>S</mi><mi>C</mi><mi>L</mi></mrow></semantics></math></inline-formula> algorithm has better detection performance over the semi-supervised algorithm and the traditional energy detection algorithm. When the number of labeled samples used is only 10% of the supervised algorithm and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>N</mi><mi>R</mi></mrow></semantics></math></inline-formula> is higher than −12 dB, the detection probability of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>S</mi><mi>C</mi><mi>L</mi></mrow></semantics></math></inline-formula> algorithm is higher than 97%, which is slightly lower than the supervised algorithm. |
| first_indexed | 2024-03-11T06:38:57Z |
| format | Article |
| id | doaj.art-86f7049c7e174b45adb1a5682dc4092b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:38:57Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-86f7049c7e174b45adb1a5682dc4092b2023-11-17T10:43:49ZengMDPI AGElectronics2079-92922023-03-01126131710.3390/electronics12061317Spectrum Sensing Algorithm Based on Self-Supervised Contrast LearningXinyu Li0Zhijin Zhao1Yupei Zhang2Shilian Zheng3Shaogang Dai4School of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310020, ChinaSchool of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310020, ChinaSchool of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310020, ChinaSchool of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310020, ChinaSchool of Electronic and Information, Hangzhou Dianzi University, Hangzhou 310020, ChinaThe traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in order to solve this problem, and a spectrum sensing algorithm based on self-supervised contrast learning (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>S</mi><mi>C</mi><mi>L</mi></mrow></semantics></math></inline-formula>) is proposed. The algorithm mainly includes two stages: pre-training and fine-tuning. In the pre-training stage, according to the characteristics of communication signals, data augmentation methods are designed to obtain the pre-trained positive sample pairs, and the features of the positive sample pairs of unlabeled samples are extracted by self-supervised contrast learning to obtain the feature extractor. In the fine-tuning stage, the parameters of the feature extraction layer are frozen, and a small number of labeled samples are used to update the parameters of the classification layer, and the features and labels are connected to get the spectrum sensing classifier. The simulation results demonstrate that the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>S</mi><mi>C</mi><mi>L</mi></mrow></semantics></math></inline-formula> algorithm has better detection performance over the semi-supervised algorithm and the traditional energy detection algorithm. When the number of labeled samples used is only 10% of the supervised algorithm and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>N</mi><mi>R</mi></mrow></semantics></math></inline-formula> is higher than −12 dB, the detection probability of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>S</mi><mi>C</mi><mi>L</mi></mrow></semantics></math></inline-formula> algorithm is higher than 97%, which is slightly lower than the supervised algorithm.https://www.mdpi.com/2079-9292/12/6/1317cognitive radiospectrum sensingself-supervised contrast learningdata augmentationlimited data |
| spellingShingle | Xinyu Li Zhijin Zhao Yupei Zhang Shilian Zheng Shaogang Dai Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning Electronics cognitive radio spectrum sensing self-supervised contrast learning data augmentation limited data |
| title | Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning |
| title_full | Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning |
| title_fullStr | Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning |
| title_full_unstemmed | Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning |
| title_short | Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning |
| title_sort | spectrum sensing algorithm based on self supervised contrast learning |
| topic | cognitive radio spectrum sensing self-supervised contrast learning data augmentation limited data |
| url | https://www.mdpi.com/2079-9292/12/6/1317 |
| work_keys_str_mv | AT xinyuli spectrumsensingalgorithmbasedonselfsupervisedcontrastlearning AT zhijinzhao spectrumsensingalgorithmbasedonselfsupervisedcontrastlearning AT yupeizhang spectrumsensingalgorithmbasedonselfsupervisedcontrastlearning AT shilianzheng spectrumsensingalgorithmbasedonselfsupervisedcontrastlearning AT shaogangdai spectrumsensingalgorithmbasedonselfsupervisedcontrastlearning |