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...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.89218-89225
Main Authors: Ke, Da, Huang, Zhitao, Wang, Xiang, Li, Xueqiong
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Cognitive radio
Convolution
Correlation
Deep learning
Feature extraction
Logic gates
Machine learning
Signal detection
Signal to noise ratio
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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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2926296