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
스펙트럼 공유를 위한 무선 전파 추정 프로세스를 지원하려면 스펙트럼 데이터베이스가 필요합니다. 특히, 측정 기반 스펙트럼 데이터베이스는 주요 사용자로부터 전송된 신호 전력 등 관측된 무선 환경 정보를 저장하여 매우 효율적인 스펙트럼 공유를 달성합니다. 그러나 주어진 정사각형 메쉬에서 평균 수신 신호 전력을 계산할 때 메쉬 내 관측 위치의 편향은 지형 및 건물의 영향으로 인해 통계의 정확도를 크게 저하시킵니다. 본 논문에서는 메쉬 클러스터링을 이용하여 통계치를 결정하는 방법을 제안한다. 제안하는 방법은 측정된 데이터의 특징 벡터를 클러스터링한다. k-평균 및 가우스 혼합 모델 방법. 시뮬레이션 결과, 제안하는 방법은 스펙트럼 데이터베이스에 존재하는 데이터의 양이 매우 적더라도 측정값과 통계적으로 처리된 값 사이의 오차를 줄일 수 있음을 보여주었다.
Rei HASEGAWA
the University of Electro-Communications
Keita KATAGIRI
the University of Electro-Communications
Koya SATO
the Tokyo University of Science
Takeo FUJII
the University of Electro-Communications
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Rei HASEGAWA, Keita KATAGIRI, Koya SATO, Takeo FUJII, "Low Storage, but Highly Accurate Measurement-Based Spectrum Database via Mesh Clustering" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 10, pp. 2152-2161, October 2018, doi: 10.1587/transcom.2017NEP0007.
Abstract: Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017NEP0007/_p
부
@ARTICLE{e101-b_10_2152,
author={Rei HASEGAWA, Keita KATAGIRI, Koya SATO, Takeo FUJII, },
journal={IEICE TRANSACTIONS on Communications},
title={Low Storage, but Highly Accurate Measurement-Based Spectrum Database via Mesh Clustering},
year={2018},
volume={E101-B},
number={10},
pages={2152-2161},
abstract={Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.},
keywords={},
doi={10.1587/transcom.2017NEP0007},
ISSN={1745-1345},
month={October},}
부
TY - JOUR
TI - Low Storage, but Highly Accurate Measurement-Based Spectrum Database via Mesh Clustering
T2 - IEICE TRANSACTIONS on Communications
SP - 2152
EP - 2161
AU - Rei HASEGAWA
AU - Keita KATAGIRI
AU - Koya SATO
AU - Takeo FUJII
PY - 2018
DO - 10.1587/transcom.2017NEP0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2018
AB - Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.
ER -