Research on the construction of radio environment map based on revised spatial interpolation

Radio Environment Map(REM) provides accurate or comprehensive information support to dynamic spectrum access of cognitive radio networks. Practically, the sample size always fails to reach the application requirements due to the limitation of environments, devices or human factors in the measurement...

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Main Authors: Zi Ran, Chang Jun, Zong Rong, Wang Ruonan, Liao Guiwen
Format: Article
Language:zho
Published: National Computer System Engineering Research Institute of China 2018-03-01
Series:Dianzi Jishu Yingyong
Subjects:
REM
Online Access:http://www.chinaaet.com/article/3000079468
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spelling doaj-915c12e22a1645f0af7d659b7a873e8f2020-11-25T01:30:21ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982018-03-0144310310710.16157/j.issn.0258-7998.1725133000079468Research on the construction of radio environment map based on revised spatial interpolationZi Ran0Chang Jun1Zong Rong2Wang Ruonan3Liao Guiwen4School of Information Science and Engineering,Yunnan University,Kunming 650500,ChinaSchool of Information Science and Engineering,Yunnan University,Kunming 650500,ChinaSchool of Information Science and Engineering,Yunnan University,Kunming 650500,ChinaSchool of Information Science and Engineering,Yunnan University,Kunming 650500,ChinaSchool of Information Science and Engineering,Yunnan University,Kunming 650500,ChinaRadio Environment Map(REM) provides accurate or comprehensive information support to dynamic spectrum access of cognitive radio networks. Practically, the sample size always fails to reach the application requirements due to the limitation of environments, devices or human factors in the measurement of field data. Hence, research on the technique of Spatial Interpolation, which can expand the discrete data into surface data, is great of application value. Comparing to the traditional MSM algorithm, this paper presents the RMSM algorithm, which is improved by modifying the weights, flexibly using the local data features, and efficiently neighbor searching. Experiments are conducted in a 15 m×20 m area,showing the obvious improvement of RMSM algorithm which reduces its error by 1.96 dB and enhances the robustness by 55.37%.http://www.chinaaet.com/article/3000079468REMcognitive radiospatial interpolation
collection DOAJ
language zho
format Article
sources DOAJ
author Zi Ran
Chang Jun
Zong Rong
Wang Ruonan
Liao Guiwen
spellingShingle Zi Ran
Chang Jun
Zong Rong
Wang Ruonan
Liao Guiwen
Research on the construction of radio environment map based on revised spatial interpolation
Dianzi Jishu Yingyong
REM
cognitive radio
spatial interpolation
author_facet Zi Ran
Chang Jun
Zong Rong
Wang Ruonan
Liao Guiwen
author_sort Zi Ran
title Research on the construction of radio environment map based on revised spatial interpolation
title_short Research on the construction of radio environment map based on revised spatial interpolation
title_full Research on the construction of radio environment map based on revised spatial interpolation
title_fullStr Research on the construction of radio environment map based on revised spatial interpolation
title_full_unstemmed Research on the construction of radio environment map based on revised spatial interpolation
title_sort research on the construction of radio environment map based on revised spatial interpolation
publisher National Computer System Engineering Research Institute of China
series Dianzi Jishu Yingyong
issn 0258-7998
publishDate 2018-03-01
description Radio Environment Map(REM) provides accurate or comprehensive information support to dynamic spectrum access of cognitive radio networks. Practically, the sample size always fails to reach the application requirements due to the limitation of environments, devices or human factors in the measurement of field data. Hence, research on the technique of Spatial Interpolation, which can expand the discrete data into surface data, is great of application value. Comparing to the traditional MSM algorithm, this paper presents the RMSM algorithm, which is improved by modifying the weights, flexibly using the local data features, and efficiently neighbor searching. Experiments are conducted in a 15 m×20 m area,showing the obvious improvement of RMSM algorithm which reduces its error by 1.96 dB and enhances the robustness by 55.37%.
topic REM
cognitive radio
spatial interpolation
url http://www.chinaaet.com/article/3000079468
work_keys_str_mv AT ziran researchontheconstructionofradioenvironmentmapbasedonrevisedspatialinterpolation
AT changjun researchontheconstructionofradioenvironmentmapbasedonrevisedspatialinterpolation
AT zongrong researchontheconstructionofradioenvironmentmapbasedonrevisedspatialinterpolation
AT wangruonan researchontheconstructionofradioenvironmentmapbasedonrevisedspatialinterpolation
AT liaoguiwen researchontheconstructionofradioenvironmentmapbasedonrevisedspatialinterpolation
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