Blind Detection Techniques for Non-Cooperative Communication Signals Based on Deep Learning
The performance of existing signal detection methods depends heavily on the amount of prior information acquired by the sensor of interest. Therefore, to improve cognitive radio-based detection in low-signal-to-noise (SNR) environments, we propose a deep learning method-based passive signal detectio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8753512/ |
| Summary: | The performance of existing signal detection methods depends heavily on the amount of prior information acquired by the sensor of interest. Therefore, to improve cognitive radio-based detection in low-signal-to-noise (SNR) environments, we propose a deep learning method-based passive signal detection. A convolution neural network (CNN) and the long short-term memory (LSTM) approach are used to extract the frequency and time domain features of the signal. Our method can detect signal when little to none prior information exists. The simulation experiments verify the probability of detection for our method. The results show that our method is about 4.5-5.5 dB better than a traditional blind detection algorithm under different SNR environments. |
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| ISSN: | 2169-3536 |