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Spectrum sensing based on adaptive sampling of received signal
Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive samp...
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| Published in: | EURASIP journal on wireless communications and networking 2021-07, Vol.2021 (1), p.1-17, Article 156 |
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| cites | cdi_FETCH-LOGICAL-c380t-901aa1dee9ec7e59a57b74e86fc113f8302458f329c4b110d3880c6e0ba134673 |
| container_end_page | 17 |
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| container_title | EURASIP journal on wireless communications and networking |
| container_volume | 2021 |
| creator | Miao, Jiawu Tan, Youheng Zhang, Yangying Li, Yuebo Mu, Junsheng Jing, Xiaojun |
| description | Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme. |
| doi_str_mv | 10.1186/s13638-021-02027-w |
| format | article |
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Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. 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| source | Publicly Available Content Database; Springer Nature - SpringerLink Journals - Fully Open Access |
| subjects | Accuracy Adaptive sampling Artificial neural networks Communications Engineering Communications networks Energy detector Engineering Evolutional Trends of Intelligent IoT in 5G Era Experiments False alarms Information Systems Applications (incl.Internet) Internet of Things Networks Sensors Signal to noise ratio Signal,Image and Speech Processing Simulation Spectrum allocation Spectrum sensing Wireless communications Wireless networks |
| title | Spectrum sensing based on adaptive sampling of received signal |
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