Low storage, but highly accurate measurement-based spectrum database via mesh clustering

Rei Hasegawa, Keita Katagiri, Koya Sato, Takeo Fujii

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)2152-2161
Number of pages10
JournalIEICE Transactions on Communications
Issue number10
Publication statusPublished - Oct 2018



  • Clustering
  • Cognitive radio
  • Spectrum database
  • Spectrum sensing
  • Spectrum sharing

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