期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 11, 期 3, 页码 498-502出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2021.3133883
关键词
Signal to noise ratio; Feature extraction; Sensors; Training; Adversarial machine learning; Covariance matrices; Adaptation models; Machine learning; adversarial learning; SNR adaptation; cognitive radio; spectrum sensing
资金
- National Key Research and Development Program of China [2019YFB1802703]
- Shanghai Super Postdoctoral Incentive Program [2020000125]
- Open-End Fund Project of the 54th Research Institute of CETC [SXX21104X009]
The paper investigates the issue of spectrum sensing, where classical methods and machine learning methods fail to adapt to new signal-to-noise ratio environments. The authors propose a new adversarial learning method that improves model adaptability by designing coupled neural networks. Simulation results demonstrate significant improvement in spectrum sensing error rate compared to existing methods.
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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