IEICE TRANSACTIONS on Communications
Convolutional Neural Networks for Pilot-Induced Cyclostationarity Based OFDM Signals Spectrum Sensing in Full-Duplex Cognitive Radio
Summary :
The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E103-B No.1 pp.91-102
- Publication Date
- 2020/01/01
- Publicized
- 2019/07/16
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018EBP3253
- Type of Manuscript
- PAPER
- Category
- Terrestrial Wireless Communication/Broadcasting Technologies
Authors
Hang LIU
The University of Electro-Communications
Xu ZHU
Toshiba Corporation
Takeo FUJII
The University of Electro-Communications
Keyword
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Hang LIU, Xu ZHU, Takeo FUJII, "Convolutional Neural Networks for Pilot-Induced Cyclostationarity Based OFDM Signals Spectrum Sensing in Full-Duplex Cognitive Radio" in IEICE TRANSACTIONS on Communications,
vol. E103-B, no. 1, pp. 91-102, January 2020, doi: 10.1587/transcom.2018EBP3253.
Abstract: The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3253/_p
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@ARTICLE{e103-b_1_91,
author={Hang LIU, Xu ZHU, Takeo FUJII, },
journal={IEICE TRANSACTIONS on Communications},
title={Convolutional Neural Networks for Pilot-Induced Cyclostationarity Based OFDM Signals Spectrum Sensing in Full-Duplex Cognitive Radio},
year={2020},
volume={E103-B},
number={1},
pages={91-102},
abstract={The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.},
keywords={},
doi={10.1587/transcom.2018EBP3253},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - Convolutional Neural Networks for Pilot-Induced Cyclostationarity Based OFDM Signals Spectrum Sensing in Full-Duplex Cognitive Radio
T2 - IEICE TRANSACTIONS on Communications
SP - 91
EP - 102
AU - Hang LIU
AU - Xu ZHU
AU - Takeo FUJII
PY - 2020
DO - 10.1587/transcom.2018EBP3253
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E103-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2020
AB - The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.
ER -
English
