Low Storage, but Highly Accurate Measurement-Based Spectrum Database via Mesh Clustering

Rei HASEGAWA; Keita KATAGIRI; Koya SATO; Takeo FUJII

doi:10.1587/transcom.2017NEP0007

Low Storage, but Highly Accurate Measurement-Based Spectrum Database via Mesh Clustering

Rei HASEGAWA, Keita KATAGIRI, Koya SATO, Takeo FUJII

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Summary :

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.

Publication: IEICE TRANSACTIONS on Communications Vol.E101-B No.10 pp.2152-2161

Publication Date: 2018/10/01

Publicized: 2018/04/13

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2017NEP0007

Type of Manuscript: Special Section PAPER (Special Section on Wireless Distributed Networks for IoT Era)

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Authors

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

Keyword

cognitive radio, spectrum sensing, spectrum sharing, spectrum database, clustering

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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},}

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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 -