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
변조 신호 검출은 스펙트럼 센싱의 핵심 기술로 다양한 무선 통신 시스템에서 급속히 발전하고 있다. 무선 채널 잡음의 비가우시안 통계, 특히 시간/주파수 영역의 펄스 특성을 다루기 위해 본 논문에서는 IGDM(Information Geometry Difference Mapping)을 기반으로 알파 안정 분포 하에서 신호 검출 문제를 해결하는 방법을 제안합니다. α-안정) 잡음을 제거하고 낮은 GSNR(일반 신호 대 잡음비)에서 성능을 향상시킵니다. 가우시안의 스케일 혼합은 신호의 확률 밀도 함수(PDF)를 근사화하고 관찰된 데이터의 통계적 순간을 모델링하는 데 사용됩니다. 정보 기하학의 원리를 바탕으로 다양한 유형의 데이터 PDF를 다양한 공간에 매핑합니다. 통계적 모멘트 모델을 적용하여 신호는 매니폴드 구조 내의 좌표점으로 투영됩니다. 그런 다음 기하 평균을 기반으로 이중 임계값 메커니즘을 설계하고 KLD(Kullback-Leibler Divergence)를 사용하여 좌표 간 정보 거리를 측정합니다. 비가우시안 잡음에서 다중 변조 신호를 검출하는 데 있어 IGDM의 우수성을 입증하기 위해 수치 시뮬레이션과 실험이 수행되었으며, 결과는 IGDM이 극도로 낮은 GSNR에서도 적응성과 효율성을 갖는다는 것을 보여줍니다.
Jiansheng BAI
North University of China
Jinjie YAO
North University of China
Yating HOU
North University of China
Zhiliang YANG
North University of China
Liming WANG
North University of China
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부
Jiansheng BAI, Jinjie YAO, Yating HOU, Zhiliang YANG, Liming WANG, "IGDM: An Information Geometric Difference Mapping Method for Signal Detection in Non-Gaussian Alpha-Stable Distributed Noise" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 12, pp. 1392-1401, December 2023, doi: 10.1587/transcom.2023EBP3071.
Abstract: Modulated signal detection has been rapidly advancing in various wireless communication systems as it's a core technology of spectrum sensing. To address the non-Gaussian statistical of noise in radio channels, especially its pulse characteristics in the time/frequency domain, this paper proposes a method based on Information Geometric Difference Mapping (IGDM) to solve the signal detection problem under Alpha-stable distribution (α-stable) noise and improve performance under low Generalized Signal-to-Noise Ratio (GSNR). Scale Mixtures of Gaussians is used to approximate the probability density function (PDF) of signals and model the statistical moments of observed data. Drawing on the principles of information geometry, we map the PDF of different types of data into manifold space. Through the application of statistical moment models, the signal is projected as coordinate points within the manifold structure. We then design a dual-threshold mechanism based on the geometric mean and use Kullback-Leibler divergence (KLD) to measure the information distance between coordinates. Numerical simulations and experiments were conducted to prove the superiority of IGDM for detecting multiple modulated signals in non-Gaussian noise, the results show that IGDM has adaptability and effectiveness under extremely low GSNR.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2023EBP3071/_p
부
@ARTICLE{e106-b_12_1392,
author={Jiansheng BAI, Jinjie YAO, Yating HOU, Zhiliang YANG, Liming WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={IGDM: An Information Geometric Difference Mapping Method for Signal Detection in Non-Gaussian Alpha-Stable Distributed Noise},
year={2023},
volume={E106-B},
number={12},
pages={1392-1401},
abstract={Modulated signal detection has been rapidly advancing in various wireless communication systems as it's a core technology of spectrum sensing. To address the non-Gaussian statistical of noise in radio channels, especially its pulse characteristics in the time/frequency domain, this paper proposes a method based on Information Geometric Difference Mapping (IGDM) to solve the signal detection problem under Alpha-stable distribution (α-stable) noise and improve performance under low Generalized Signal-to-Noise Ratio (GSNR). Scale Mixtures of Gaussians is used to approximate the probability density function (PDF) of signals and model the statistical moments of observed data. Drawing on the principles of information geometry, we map the PDF of different types of data into manifold space. Through the application of statistical moment models, the signal is projected as coordinate points within the manifold structure. We then design a dual-threshold mechanism based on the geometric mean and use Kullback-Leibler divergence (KLD) to measure the information distance between coordinates. Numerical simulations and experiments were conducted to prove the superiority of IGDM for detecting multiple modulated signals in non-Gaussian noise, the results show that IGDM has adaptability and effectiveness under extremely low GSNR.},
keywords={},
doi={10.1587/transcom.2023EBP3071},
ISSN={1745-1345},
month={December},}
부
TY - JOUR
TI - IGDM: An Information Geometric Difference Mapping Method for Signal Detection in Non-Gaussian Alpha-Stable Distributed Noise
T2 - IEICE TRANSACTIONS on Communications
SP - 1392
EP - 1401
AU - Jiansheng BAI
AU - Jinjie YAO
AU - Yating HOU
AU - Zhiliang YANG
AU - Liming WANG
PY - 2023
DO - 10.1587/transcom.2023EBP3071
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2023
AB - Modulated signal detection has been rapidly advancing in various wireless communication systems as it's a core technology of spectrum sensing. To address the non-Gaussian statistical of noise in radio channels, especially its pulse characteristics in the time/frequency domain, this paper proposes a method based on Information Geometric Difference Mapping (IGDM) to solve the signal detection problem under Alpha-stable distribution (α-stable) noise and improve performance under low Generalized Signal-to-Noise Ratio (GSNR). Scale Mixtures of Gaussians is used to approximate the probability density function (PDF) of signals and model the statistical moments of observed data. Drawing on the principles of information geometry, we map the PDF of different types of data into manifold space. Through the application of statistical moment models, the signal is projected as coordinate points within the manifold structure. We then design a dual-threshold mechanism based on the geometric mean and use Kullback-Leibler divergence (KLD) to measure the information distance between coordinates. Numerical simulations and experiments were conducted to prove the superiority of IGDM for detecting multiple modulated signals in non-Gaussian noise, the results show that IGDM has adaptability and effectiveness under extremely low GSNR.
ER -