Skip navigation

A wall imperfection channel model for signal level prediction and its impact on smart antenna systems for indoor infrastructure WLAN

A wall imperfection channel model for signal level prediction and its impact on smart antenna systems for indoor infrastructure WLAN

Nasr, K. M. ORCID: 0000-0002-8604-6274, Costen, F. and Barton, S.K. (2005) A wall imperfection channel model for signal level prediction and its impact on smart antenna systems for indoor infrastructure WLAN. IEEE Transactions on Antennas and Propagation, 53 (11). pp. 3767-3775. ISSN 0018-926X (doi:https://doi.org/10.1109/TAP.2005.858593)

Full text not available from this repository. (Request a copy)

Abstract

This paper presents a novel approach for the estimation of the local average signal level in an indoor environment based on a wall imperfection model. A ray-tracing tool based on the method of images with angular information, is first used to estimate the distribution of field strength (or coverage) in an arbitrary environment. The concept of spatial sampling is highlighted. 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 presented based on the wall imperfection model to reduce the computation time. The impact of the wall imperfection model on the patterns of a smart antenna system serving multiple users in an infrastructure WLAN is studied.

Item Type: Article
Uncontrolled Keywords: smart antennas, space division multiple access (SDMA), spatial indoor channel modeling, wall imperfections, WLAN
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/25341

Actions (login required)

View Item View Item