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- Title
A Blind Spectrum Sensing Method Based on Deep Learning.
- Authors
Yang, Kai; Huang, Zhitao; Wang, Xiang; Li, Xueqiong
- Abstract
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.
- Subjects
SPECTRUM analysis; DEEP learning; ARTIFICIAL neural networks; PROBLEM solving; SIGNAL-to-noise ratio
- Publication
Sensors (14248220), 2019, Vol 19, Issue 10, p2270
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s19102270