IEICE TRANSACTIONS on Communications
A Spectrum Sensing Algorithm for OFDM Signal Based on Deep Learning and Covariance Matrix Graph
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.12 pp.2435-2444
- Publication Date
- 2018/12/01
- Publicized
- 2018/05/30
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2017EBP3442
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Mengbo ZHANG
Electronic Countermeasure Institute
Lunwen WANG
Electronic Countermeasure Institute
Yanqing FENG
Electronic Countermeasure Institute
Haibo YIN
Electronic Countermeasure Institute
Keyword
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Mengbo ZHANG, Lunwen WANG, Yanqing FENG, Haibo YIN, "A Spectrum Sensing Algorithm for OFDM Signal Based on Deep Learning and Covariance Matrix Graph" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 12, pp. 2435-2444, December 2018, doi: 10.1587/transcom.2017EBP3442.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3442/_p
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@ARTICLE{e101-b_12_2435,
author={Mengbo ZHANG, Lunwen WANG, Yanqing FENG, Haibo YIN, },
journal={IEICE TRANSACTIONS on Communications},
title={A Spectrum Sensing Algorithm for OFDM Signal Based on Deep Learning and Covariance Matrix Graph},
year={2018},
volume={E101-B},
number={12},
pages={2435-2444},
abstract={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.},
keywords={},
doi={10.1587/transcom.2017EBP3442},
ISSN={1745-1345},
month={December},}
Copy
TY - JOUR
TI - A Spectrum Sensing Algorithm for OFDM Signal Based on Deep Learning and Covariance Matrix Graph
T2 - IEICE TRANSACTIONS on Communications
SP - 2435
EP - 2444
AU - Mengbo ZHANG
AU - Lunwen WANG
AU - Yanqing FENG
AU - Haibo YIN
PY - 2018
DO - 10.1587/transcom.2017EBP3442
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2018
AB - 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.
ER -
English
