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

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Published inInternational journal of digital multimedia broadcasting Vol. 2020; no. 2020; pp. 1 - 10
Main Authors Pan, Guangliang, Lin, Fei, Li, Jun
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects
Algorithms
Artificial neural networks
Autocorrelation
Classification
Cognitive radio
Computer simulation
Deep learning
Detection
False alarms
Fast Fourier transformations
Feature extraction
Gray scale
Hypotheses
Machine learning
Methods
Neural networks
Noise
Orthogonal Frequency Division Multiplexing
Radio spectra
Signal processing
Signal to noise ratio
Simulation
Spectrum allocation
Support vector machines
Wireless communications
Wireless networks
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Summary: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.
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ISSN:1687-7578
1687-7586
DOI:10.1155/2020/5069021