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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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| Published in: | Sensors (Basel, Switzerland) Switzerland), 2019-05, Vol.19 (10), p.2270 |
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| Main Authors: | , , , |
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| cites | cdi_FETCH-LOGICAL-c535t-3200bbd6da28e1d0e3d59396502c6e0f82992189be3c14ce5b9520c0ca55879a3 |
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| container_issue | 10 |
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| container_title | Sensors (Basel, Switzerland) |
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| creator | Yang, Kai Huang, Zhitao Wang, Xiang Li, Xueqiong |
| description | 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. |
| doi_str_mv | 10.3390/s19102270 |
| format | article |
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| 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 |
| title | A Blind Spectrum Sensing Method Based on Deep Learning |
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