For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
A Spectrum Sensing Algorithm for OFDM Signal Based on Deep Learning and Covariance Matrix Graph
Mengbo ZHANG Lunwen WANG Yanqing FENG Haibo YIN
IEICE TRANSACTIONS on Communications
Publication Date: 2018/12/01
Online ISSN: 1745-1345
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
cognitive radio, OFDM, spectrum sensing, covariance matrix, convolutional neural network,
Full Text: PDF(2.1MB)>>
Spectrum sensing is the first task performed by cognitive radio (CR) networks. In this paper we propose a spectrum sensing algorithm for orthogonal frequency division multiplex (OFDM) signal based on deep learning and covariance matrix graph. The advantage of deep learning in image processing is applied to the spectrum sensing of OFDM signals. We start by building the spectrum sensing model of OFDM signal, and then analyze structural characteristics of covariance matrix (CM). Once CM has been normalized and transformed into a gray level representation, the gray scale map of covariance matrix (GSM-CM) is established. Then, the convolutional neural network (CNN) is designed based on the LeNet-5 network, which is used to learn the training data to obtain more abstract features hierarchically. Finally, the test data is input into the trained spectrum sensing network model, based on which spectrum sensing of OFDM signals is completed. Simulation results show that this method can complete the spectrum sensing task by taking advantage of the GSM-CM model, which has better spectrum sensing performance for OFDM signals under low SNR than existing methods.