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Entropy‐Greedy Node Selection Algorithm in Spectrum Map Construction.

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Title: Entropy‐Greedy Node Selection Algorithm in Spectrum Map Construction.
Authors: Mo, Ruoyu1,2 (AUTHOR), Zhang, Jianzhao2 (AUTHOR) jianzhao63s@nudt.edu.cn, Yao, Changhua1 (AUTHOR), Si, Chengcheng2 (AUTHOR)
Source: IET Communications (Wiley-Blackwell). Jan2025, Vol. 19 Issue 1, p1-14. 14p.
Subjects: Spectrum analysis instruments, Signal reconstruction, Data visualization software, Signal detection, Maximum entropy method, Acquisition of data
Abstract: Spectrum maps are visualization tools that reflect the underlying spectral environment, enabling advanced functions such as spectrum decision‐making and emitter identification. To enhance mapping accuracy and optimize resource utilization, this study addresses the sensor node selection problem in ground‐based sensing scenarios. We propose an entropy‐greedy node selection (EGNS) framework that employs a two‐stage scheduling strategy: the first stage performs coarse sensing via spatial sector partitioning to obtain an initial estimate of emitter locations, and the second stage executes an enhanced greedy selection algorithm to iteratively minimize the signal reconstruction error. Simulation results on real‐world spectrum datasets show that the proposed method achieves superior reconstruction accuracy and lower sensing costs compared to conventional sampling approaches, making it well‐suited for dynamic electromagnetic monitoring applications under constrained budgets. [ABSTRACT FROM AUTHOR]
Copyright of IET Communications (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 190328083
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Entropy‐Greedy Node Selection Algorithm in Spectrum Map Construction.
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  Data: <searchLink fieldCode="AR" term="%22Mo%2C+Ruoyu%22">Mo, Ruoyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jianzhao%22">Zhang, Jianzhao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jianzhao63s@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yao%2C+Changhua%22">Yao, Changhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Si%2C+Chengcheng%22">Si, Chengcheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22IET+Communications+%28Wiley-Blackwell%29%22">IET Communications (Wiley-Blackwell)</searchLink>. Jan2025, Vol. 19 Issue 1, p1-14. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Spectrum+analysis+instruments%22">Spectrum analysis instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Data+visualization+software%22">Data visualization software</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+entropy+method%22">Maximum entropy method</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Spectrum maps are visualization tools that reflect the underlying spectral environment, enabling advanced functions such as spectrum decision‐making and emitter identification. To enhance mapping accuracy and optimize resource utilization, this study addresses the sensor node selection problem in ground‐based sensing scenarios. We propose an entropy‐greedy node selection (EGNS) framework that employs a two‐stage scheduling strategy: the first stage performs coarse sensing via spatial sector partitioning to obtain an initial estimate of emitter locations, and the second stage executes an enhanced greedy selection algorithm to iteratively minimize the signal reconstruction error. Simulation results on real‐world spectrum datasets show that the proposed method achieves superior reconstruction accuracy and lower sensing costs compared to conventional sampling approaches, making it well‐suited for dynamic electromagnetic monitoring applications under constrained budgets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IET Communications (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190328083
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    Identifiers:
      – Type: doi
        Value: 10.1049/cmu2.70112
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      – Code: eng
        Text: English
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        PageCount: 14
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    Subjects:
      – SubjectFull: Spectrum analysis instruments
        Type: general
      – SubjectFull: Signal reconstruction
        Type: general
      – SubjectFull: Data visualization software
        Type: general
      – SubjectFull: Signal detection
        Type: general
      – SubjectFull: Maximum entropy method
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
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      – TitleFull: Entropy‐Greedy Node Selection Algorithm in Spectrum Map Construction.
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            NameFull: Mo, Ruoyu
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            NameFull: Zhang, Jianzhao
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            NameFull: Yao, Changhua
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            NameFull: Si, Chengcheng
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            – D: 01
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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