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Average signal level prediction in an indoor WLAN using wall imperfection model

Average signal level prediction in an indoor WLAN using wall imperfection model

Nasr, K. M. ORCID: 0000-0002-8604-6274, Costen, F. and Barton, S.K. (2006) Average signal level prediction in an indoor WLAN using wall imperfection model. In: 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, pp. 674-678. ISBN 978-3800729098 ISSN 2166-9570 (Print), 2166-9589 (Online) (doi:https://doi.org/10.1109/PIMRC.2005.1651521)

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Abstract

This paper presents a novel approach for the estimation of the local average signal level in an arbitrary indoor environment based on a wall imperfection model. A ray-tracing tool based on the method of images with angular information, is used to estimate the distribution of field strength (or coverage) in an arbitrary environment. The spatial sampling approach for signal level distribution prediction is studied. The wall imperfection model is then introduced to study the sensitivity of the received signal level at an arbitrary location to imperfect wall positioning and electromagnetic material properties. An alternative approach to estimate the local mean signal level at a particular point is proposed based on the introduced wall imperfection model to reduce the computation time compared to the spatial sampling approach.

Item Type: Conference Proceedings
Title of Proceedings: 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications
Uncontrolled Keywords: wireless LAN, predictive models, ray tracing, sampling methods, delay, indoor environments, electromagnetic modeling, material properties, bandwidth, computer vision
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 02 Oct 2019 12:36
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/25354

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