Online assessment of sensing performance in experimental spectrum sensing platforms

2014 ◽  
Author(s):  
Stratos Keranidis ◽  
Virgilios Passas ◽  
Kostas Chounos ◽  
Wei Liu ◽  
Thanasis Korakis ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Experimental Spectrum ◽  
Online Assessment ◽  
Sensing Platforms ◽  
Sensing Performance

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

scholarly journals A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093313 ◽  
Author(s):  
Tangsen Huang ◽  
Xiangdong Yin ◽  
Qingjiao Cao
Keyword(s):  
Real Time ◽  
Spectrum Sensing ◽  
Computation Time ◽  
Time Spectrum ◽  
Fusion Center ◽  
Local Data ◽  
Green Communication ◽  
Processing Efficiency ◽  
The Real ◽  
Sensing Performance

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.

scholarly journals A Novel Sensing Strategy Based on Energy Detector for Spectrum Sensing

2019 ◽  
Vol 9 (21) ◽  
pp. 4634 ◽  
Author(s):  
Hai Huang ◽  
Jia Zhu ◽  
Junsheng Mu
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Energy Detector ◽  
Accuracy Improvement ◽  
Classical Scheme ◽  
Sensing Performance

Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in this paper, where an improved tradeoff among detection probability, false-alarm probability and available throughput is obtained based on the energy detector. We provide the optimal sensing performance and exhibit its superiority in theory compared with the classical scheme. Finally, simulations validate the conclusions drawn in this paper.

scholarly journals A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

2020 ◽  
Author(s):  
Jin Lu ◽  
Ming Huang ◽  
Jingjing Yang
Keyword(s):  
Covariance Matrix ◽  
Fading Channels ◽  
Stochastic Resonance ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Additive White Gaussian Noise ◽  
Ratio Test ◽  
Sample Covariance ◽  
Simulation Results ◽  
Sensing Performance

Abstract Cognitive radio (CR) is a dynamic spectrum sharing technology designed to reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

scholarly journals A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

2020 ◽  
Author(s):  
Jin Lu ◽  
Ming Huang ◽  
Jingjing Yang
Keyword(s):  
Covariance Matrix ◽  
Fading Channels ◽  
Stochastic Resonance ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Additive White Gaussian Noise ◽  
Ratio Test ◽  
Sample Covariance ◽  
Simulation Results ◽  
Sensing Performance

Abstract Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

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