The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
인지 무선(CR)에서 OFDM(직교 주파수 분할 다중화) 시스템의 스펙트럼 감지는 항상 어려운 과제였으며, 특히 전이중(FD) 모드를 활용하는 사용자 단말기의 경우 더욱 그렇습니다. 본 발명에서는 사용자 단말로부터 심각한 자기 간섭이 발생하는 경우에도 성공적으로 수행될 수 있는 개선된 FD 스펙트럼 감지 기법을 제안한다. "분류 변환 감지" 프레임워크를 기반으로 OFDM 파일럿이 생성한 순환정리 주기도를 이미지 형식으로 표시합니다. 이러한 이미지는 CNN의 강력한 이미지 인식 덕분에 분류를 위해 CNN(컨볼루션 신경망)에 연결됩니다. 더 중요한 것은 잔류 자기 간섭, 잡음 오염 및 채널 페이딩에 대한 스펙트럼 감지를 실현하기 위해 CR 관련 수정된 훈련 데이터베이스가 제안된 적대적 훈련을 사용했다는 것입니다. 우리는 탐지 성능과 컴퓨팅 능력의 균형을 맞추기 위해 CNN의 다양한 아키텍처와 입력 이미지의 다양한 해상도에서 나타나는 성능을 분석했습니다. 제안된 스펙트럼 감지 방식에 적합하면서 자체 전송의 이점을 누릴 수 있는 CR 전송 단말의 신호 구조 설계 계획을 제안했습니다. 시뮬레이션 결과는 우리의 방법이 FD 시스템에 대해 탁월한 감지 기능을 가지고 있음을 입증합니다. 또한, 우리의 방법은 기존 방법보다 더 높은 검출 정확도를 달성합니다.
Hang LIU
The University of Electro-Communications
Xu ZHU
Toshiba Corporation
Takeo FUJII
The University of Electro-Communications
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부
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
부
@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},}
부
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 -