Study of Cognitive Radio Spectrum Sensing Algorithm Based on Compressed Sensing

2014 ◽  
Vol 602-605 ◽  
pp. 3639-3642 ◽  
Author(s):  
Chua Nan Cui
Keyword(s):  
Signal Processing ◽  
Cognitive Radio ◽  
Compressed Sensing ◽  
Spectrum Sensing ◽  
Basic Principle ◽  
Radio Spectrum ◽  
Research Results ◽  
Innovative Research ◽  
Sensing Technology ◽  
Key Technologies

As the latest progress in modern signal processing, Compressed Sensing (CS) has great potential for application in the field of cognitive radio and other fields. In this paper, the basic principle of compressed sensing technology and its application in cognitive radio spectrum perception key technologies are studied, achieving innovative research results. Spectrum sensing technology in cognitive radio

The New Research of Spectrum Sensing Technology for Cognitive Radio

2014 ◽  
Vol 945-949 ◽  
pp. 2301-2305
Author(s):  
Yi Peng ◽  
Yan Jun Wang
Keyword(s):  
Cognitive Radio ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Communication Technology ◽  
Spectrum Sharing ◽  
Rapid Development ◽  
Current Model ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Sensing Technology

With the rapid development of wireless communication technology, the shortage of spectrum resources is becoming more and more serious, and may even become a bottleneck restricting of the development wireless communication technology in the future. Now, Spectrum sensing technology, spectrum sharing technology and spectrum management technology is the three core technologies of cognitive radio spectrum,and sensing technology is to implement the follow-up of spectrum sharing and the premise of spectrum management.So mainly to the current model of the cognitive radio spectrum sensing technology,to make a classification and comparison, finally it is concluded that cognitive users under the environment of higher signal-to-noise ratio, the better results of the perceived performance.

scholarly journals A Sequential Compressed Spectrum Sensing Algorithm against SSDH Attack in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhuhua Hu ◽  
Yong Bai ◽  
Lu Cao ◽  
Mengxing Huang ◽  
Mingshan Xie
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Radio Networks ◽  
Wideband Spectrum Sensing ◽  
Sensing Technology ◽  
Key Technologies ◽  
Simulation Results

Spectrum sensing is one of the key technologies in wireless wideband communication. There are still challenges in respect of how to realize fast and robust wideband spectrum sensing technology. In this paper, a novel nonreconstructed sequential compressed wideband spectrum sensing algorithm (NSCWSS) is proposed. Firstly, the algorithm uses a sequential spectrum sensing method based on history memory and reputation to ensure the robustness of the algorithm. Secondly, the algorithm uses the strategy of compressed sensing without reconstruction, which thus ensures the sensing agility of the algorithm. The algorithm is simulated and analyzed by using the centralized cooperative sensing. The theoretical analysis and simulation results reveal that, under the condition of ensuring the certain detection probability, the proposed algorithm effectively reduces complex computation of signal reconstruction, significantly reducing the wideband spectrum sampling rate. At the same time, in the cognitive wideband communication scenarios, the algorithm also achieves a better defense against the SSDF attack in spectrum sensing.

scholarly journals Cognitive Radio: Spectrum Sensing Problems in Signal Processing

2012 ◽  
Vol 40 (16) ◽  
pp. 37-40 ◽  
Author(s):  
J. ChristopherClement ◽  
Kishore V. Krishnan ◽  
A. Bagubali
Keyword(s):  
Signal Processing ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Radio Spectrum

scholarly journals Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 129
Author(s):  
Mingdong Xu ◽  
Zhendong Yin ◽  
Yanlong Zhao ◽  
Zhilu Wu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Radio Spectrum ◽  
Local Information ◽  
Channel State ◽  
Single Node

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

scholarly journals Cognitive Radio Spectrum Sensing Technique Using M-ary QAM for Different Fading Environments

2017 ◽  
Vol 12 (4) ◽  
pp. 43-52
Author(s):  
Jai Sukh Paul Singh ◽  
Mritunjay Kumar Rai ◽  
Gulshan Kumar ◽  
Hye-jin Kim
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Radio Spectrum ◽  
Fading Environments
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