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Spectrum sensing based on adaptive sampling of received signal

Authors
  • Miao, Jiawu1
  • Tan, Youheng1
  • Zhang, Yangying1
  • Li, Yuebo1
  • Mu, Junsheng1
  • Jing, Xiaojun1
  • 1 Beijing University of Posts and Telecommunications, Beijing, People’s Republic of China , Beijing (China)
Type
Published Article
Journal
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer International Publishing
Publication Date
Jul 14, 2021
Volume
2021
Issue
1
Identifiers
DOI: 10.1186/s13638-021-02027-w
Source
Springer Nature
Keywords
Disciplines
  • Evolutional Trends of Intelligent IoT in 5G Era
License
Green

Abstract

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.

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