Abstract
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.
Highlights
The emergence of the fifth-generation mobile communication network (5G) [1] has greatly promoted the development of broadband wireless communication [2], and orthogonal frequency division multiplex (OFDM) is one of the popular physical transmission technologies for wireless communication [3, 4]
Traditional spectrum sensing is mainly done by mathematical methods of signal processing, including energy detection [11], matched filter detection [12], and cyclostationary feature detection [13]. [14] proposed a spectrum sensing method based on correlation detection, the correlation of cyclic prefix (CP) was used in OFDM, and the sampled data was subjected to correlation operation
The contributions of this paper are summarized as follows: (i) We analyze the cyclic autocorrelation of an OFDM signal, using the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM) to achieve cyclic spectrum and transforming the spectrum sensing problem into an image processing and recognition problem (ii) We convert the cyclic spectrum into a gray scale image, transforming the spectral perception problem into an image processing problem (iii) We adopt an improved convolutional neural network (CNN) based on LeNet-5 to generate a spectrum sensing model to complete spectrum sensing
Summary
The emergence of the fifth-generation mobile communication network (5G) [1] has greatly promoted the development of broadband wireless communication [2], and orthogonal frequency division multiplex (OFDM) is one of the popular physical transmission technologies for wireless communication [3, 4]. In [17], focusing on classifying different OFDM signals, authors proposed a two-step detection and identification method. (i) We analyze the cyclic autocorrelation of an OFDM signal, using the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM) to achieve cyclic spectrum and transforming the spectrum sensing problem into an image processing and recognition problem (ii) We convert the cyclic spectrum into a gray scale image, transforming the spectral perception problem into an image processing problem (iii) We adopt an improved CNN based on LeNet-5 to generate a spectrum sensing model to complete spectrum sensing.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Digital Multimedia Broadcasting
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
