Experimental verification of frequency-correlation in radio propagation characteristics for radio environment recognition

Keita Onose, Koya Sato, Kei Inage, Takeo Fujii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In order to realize highly efficient spectrum sharing, it is important to accurately recognize the radio environment for the suitable communication parameter setting, and interpolation of the statistical information on the frequency axis can be used. Some papers report that shadowing has high frequency correlated. However, conventional works only present frequency correlation analyses between several distant channels. In this paper, we evaluate frequency correlation of shadowing based on actual measurement using 5 GHz wireless LAN channels for confirming the adaptability of the frequency correlation to wireless environment recognition. In addition, we examine the possibility of interpolation of statistical information on frequency axis using inverse distance weighting (IDW).

Original languageEnglish
Title of host publication33rd International Conference on Information Networking, ICOIN 2019
PublisherIEEE Computer Society
Pages274-278
Number of pages5
ISBN (Electronic)9781538683507
DOIs
Publication statusPublished - 17 May 2019
Event33rd International Conference on Information Networking, ICOIN 2019 - Kuala Lumpur, Malaysia
Duration: 9 Jan 201911 Jan 2019

Publication series

NameInternational Conference on Information Networking
Volume2019-January
ISSN (Print)1976-7684

Conference

Conference33rd International Conference on Information Networking, ICOIN 2019
CountryMalaysia
CityKuala Lumpur
Period9/01/1911/01/19

Keywords

  • Cognitive Radio
  • Crowd Sensing
  • Frequency Correlation
  • Radio Propagation
  • Spectrum Database

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  • Cite this

    Onose, K., Sato, K., Inage, K., & Fujii, T. (2019). Experimental verification of frequency-correlation in radio propagation characteristics for radio environment recognition. In 33rd International Conference on Information Networking, ICOIN 2019 (pp. 274-278). [8718164] (International Conference on Information Networking; Vol. 2019-January). IEEE Computer Society. https://doi.org/10.1109/ICOIN.2019.8718164