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Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks.

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Title: Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks.
Authors: Jing Lei1 michelle.j.lei@gmail.com, Greenstein, Larry1, Yates, Roy1
Source: EURASIP Journal on Wireless Communications & Networking. 2010, Special section p1-9. 9p. 3 Diagrams, 11 Graphs.
Subjects: Distributed operating systems (Computers), Wireless communications, Wireless sensor networks, Wireless sensor nodes, Estimation theory
Abstract: In planning and using large-scale distributed wireless networks, knowledge of the link gain matrix can be highly valuable. If the number N of radio nodes is large, measuring N(N - 1)/2 node-to-node link gains can be prohibitive. This motivates us to devise a methodology that measures a fraction of the links and accurately estimates the rest. Our method partitions the set of transmitreceive links intomutually exclusive categories, based on the number of obstructions or walls on the path; then it derives a separate link gain model for each category. The model is derived using gain measurements on only a small fraction of the links, selected on the basis of a maximum entropy. To evaluate the new method, we use ray-tracing to compute the "true" path gains for all links in the network.We use knowledge of a subset of those gains to derive the models and then use those models to predict the remaining path gains. We do this for three different environments of distributed nodes, including an office building with many obstructing walls.We find in all cases that the partitioning method yields acceptably low path gain estimation errors with a significantly reduced number of measurements. [ABSTRACT FROM AUTHOR]
Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature 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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  Data: Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks.
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  Data: <searchLink fieldCode="DE" term="%22Distributed+operating+systems+%28Computers%29%22">Distributed operating systems (Computers)</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+nodes%22">Wireless sensor nodes</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink>
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  Data: In planning and using large-scale distributed wireless networks, knowledge of the link gain matrix can be highly valuable. If the number N of radio nodes is large, measuring N(N - 1)/2 node-to-node link gains can be prohibitive. This motivates us to devise a methodology that measures a fraction of the links and accurately estimates the rest. Our method partitions the set of transmitreceive links intomutually exclusive categories, based on the number of obstructions or walls on the path; then it derives a separate link gain model for each category. The model is derived using gain measurements on only a small fraction of the links, selected on the basis of a maximum entropy. To evaluate the new method, we use ray-tracing to compute the "true" path gains for all links in the network.We use knowledge of a subset of those gains to derive the models and then use those models to predict the remaining path gains. We do this for three different environments of distributed nodes, including an office building with many obstructing walls.We find in all cases that the partitioning method yields acceptably low path gain estimation errors with a significantly reduced number of measurements. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature 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=57314184
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        Value: 10.1155/2010/651795
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        Text: English
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        PageCount: 9
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      – SubjectFull: Distributed operating systems (Computers)
        Type: general
      – SubjectFull: Wireless communications
        Type: general
      – SubjectFull: Wireless sensor networks
        Type: general
      – SubjectFull: Wireless sensor nodes
        Type: general
      – SubjectFull: Estimation theory
        Type: general
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      – TitleFull: Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks.
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              Text: 2010
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