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
Des bases de données sur le spectre sont nécessaires pour faciliter le processus d'estimation de la propagation radioélectrique pour le partage du spectre. En particulier, une base de données de spectre basée sur des mesures permet un partage de spectre très efficace en stockant les informations sur l'environnement radio observées, telles que la puissance du signal transmis par un utilisateur principal. Cependant, lorsque la puissance moyenne du signal reçu est calculée dans un maillage carré donné, le biais des emplacements d'observation au sein du maillage dégrade fortement la précision des statistiques en raison de l'influence du terrain et des bâtiments. Cet article propose une méthode pour déterminer les statistiques en utilisant le clustering de maillage. La méthode proposée regroupe les vecteurs de caractéristiques des données mesurées en utilisant le k-moyennes et méthodes de modèle de mélange gaussien. Les résultats de simulation montrent que la méthode proposée peut réduire l'erreur entre la valeur mesurée et la valeur traitée statistiquement même si seule une petite quantité de données est disponible dans la base de données spectrale.
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
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@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},}
Copier
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 -