scholarly journals A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Guangliang Pan ◽  
Jun Li ◽  
Fei Lin
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Model Simulation ◽  
Radio Spectrum ◽  
Fast Fourier Transformation ◽  
Layer By Layer ◽  
Gray Scale ◽  
Ofdm Signal ◽  
Cyclic Spectrum

In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

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

Spectrum sensing in cognitive radio: A deep learning based model

2021 ◽  
Author(s):  
Huanlai Xing ◽  
Haoxiang Qin ◽  
Shouxi Luo ◽  
Penglin Dai ◽  
Lexi Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing

Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

2020 ◽  
Author(s):  
Rahil Sarikhani ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Recurrent Neural Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

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.

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