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." />
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Low Storage, but Highly Accurate Measurement-Based Spectrum Database via Mesh Clustering

Rei HASEGAWA
Keita KATAGIRI
Koya SATO
Takeo FUJII

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)
Category: 
Keyword: 
cognitive radio,  spectrum sensing,  spectrum sharing,  spectrum database,  clustering,  

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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.