Adversarial Learning-Based Spectrum Sensing in Cognitive Radio
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Wireless Communications Letters, 2022, 11, (3), pp. 498-502
- Issue Date:
- 2022-03-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Adversarial_Learning-Based_Spectrum_Sensing_in_Cognitive_Radio.pdf | 718.33 kB |
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In spectrum sensing, classical signal processing based sensing methods create a test statistic based on empirically statistical modeling. Recently, machine learning (ML) based methods use a neural network (NN) to learn a test statistic in a data-driven manner, but they can not well adapt to a new spectrum environment featured by a test signal-to-noise ratio (SNR) set with new SNR value(s). To address this issue, we propose a new adversarial learning based spectrum sensing method to improve the model adaptability. The key of our method is to design three coupled NNs, which can extract the universal less SNR-dependent features in the training SNR set, and use these features to infer the spectrum status in a new test SNR set. Simulation results show that the proposed method can achieve a significant performance improvement compared to the existing ML based methods and classical signal processing methods in terms of the spectrum sensing error rate.
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