A Blind Spectrum Sensing Method Based on Deep Learning

Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal i...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-05, Vol.19 (10), p.2270
Main Authors: Yang, Kai, Huang, Zhitao, Wang, Xiang, Li, Xueqiong
Format: Article
Language:English
Subjects:
Artificial intelligence
Classification
Communication
convolutional neural networks
Deep learning
International conferences
Licenses
long short-term memory
Machine learning
Methods
Neural networks
Noise
Pattern recognition
Signal to noise ratio
spectrum sensing
Time series
Wireless networks
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Summary:Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19102270