Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model

Propagation prediction and measurement is one of the foremost processes that must be taken into consideration prior to any deployment of wireless networks. This will certainly eliminate issues such as weak received signals or even not in coverage areas. In front battle area where tactical communicat...

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Main Author:	Sinnathamby, Rengiah
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/12548/1/RengiahSinnathambyMFKE2009.pdf
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id	my-utm-ep.12548
record_format	uketd_dc
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Sinnathamby, Rengiah Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model
description	Propagation prediction and measurement is one of the foremost processes that must be taken into consideration prior to any deployment of wireless networks. This will certainly eliminate issues such as weak received signals or even not in coverage areas. In front battle area where tactical communications normally consist of deployed wireless network, prediction capability is crucial to the success of operation since networks planners have limited time and access to the battle area. The continuous advent in the field of satellite mapping makes available more precise information about terrain that finally produces more accurate propagation prediction. The outdoor propagation and measurement was done at Universiti Teknologi Malaysia Skudai Campus Residential Blocks of S08, S37-S40. The WCC wireless network carrier frequency is at 2.4 GHz. Measurement was done using AirMagnet Survey Solutions while Radiowave Prediction Software (RPS) is used as outdoor propagation prediction software using COST 231 Walfisch-Ikegami Outdoor Model. Signal strength was measures around these blocks and compared with predicted received signal level using RPS.
format	Thesis
qualification_level	Master's degree
author	Sinnathamby, Rengiah
author_facet	Sinnathamby, Rengiah
author_sort	Sinnathamby, Rengiah
title	Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model
title_short	Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model
title_full	Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model
title_fullStr	Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model
title_full_unstemmed	Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model
title_sort	prediction of best coverage area for outdoor wireless propagation at 2.4 ghz using walfisch-ikegami simulation model
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/12548/1/RengiahSinnathambyMFKE2009.pdf
_version_	1747814941607329792
spelling	my-utm-ep.125482018-06-25T08:57:47Z Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model 2009-05 Sinnathamby, Rengiah TK Electrical engineering. Electronics Nuclear engineering Propagation prediction and measurement is one of the foremost processes that must be taken into consideration prior to any deployment of wireless networks. This will certainly eliminate issues such as weak received signals or even not in coverage areas. In front battle area where tactical communications normally consist of deployed wireless network, prediction capability is crucial to the success of operation since networks planners have limited time and access to the battle area. The continuous advent in the field of satellite mapping makes available more precise information about terrain that finally produces more accurate propagation prediction. The outdoor propagation and measurement was done at Universiti Teknologi Malaysia Skudai Campus Residential Blocks of S08, S37-S40. The WCC wireless network carrier frequency is at 2.4 GHz. Measurement was done using AirMagnet Survey Solutions while Radiowave Prediction Software (RPS) is used as outdoor propagation prediction software using COST 231 Walfisch-Ikegami Outdoor Model. Signal strength was measures around these blocks and compared with predicted received signal level using RPS. 2009-05 Thesis http://eprints.utm.my/id/eprint/12548/ http://eprints.utm.my/id/eprint/12548/1/RengiahSinnathambyMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering [1] Dr. J. Deibner, J. Hubner, D. Hunold, Dr. J. Voigt (2009). RPS Radiowave Propagation Simulator, RPS-v5.4. Retrieved on 2 Feb 2009 from fttp://www.actix.com [2] Theodore S. Rappaport (2002). Wireless Communications Principles And Practice (2nd ed) N.J, Prentice Hall. [3] Christoph Stamm (2001), Algorithms and Software for Radio Signal Coverage Prediction in Terrains, PhD Thesis, Swiss Federal Institute of Technology. [4] Edilberto O. Rozal and Evaldo G. Pelaes (2007). Statistical Adjustment of Walfisch-Ikegami Model based in Urban Propagation Measurements. 2007 SBMO/IEEE MTT-S International Microwave & Optoelectronics Conference (IMOC 2007) [5] Nagendra Sah, Tilak Thakur (2005), Impact of Clutters on Quality of Service in Mobile Communication Using Walfisch-lkegami Propagation Model. Personal Wireless Communications ICPWC 2005. 2005 IEEE International Conference. 23-25 Jan. 2005, Page(s):290 – 294. [6] Bernard H. Fleury, Peter E. Leuthold (1996). Radiowave Propagation in Mobile Communications: An Overview of European Research. Swiss Federal Institute of Technology, Zurich. IEEE Communications Magazine, February 1996. [7] Chad Takahashi, Zhengqing Yun, Magdy F. Iskander, Gregory Poilasne, Vaneet Pathak and Jordi Fabrega (2007). Propagation-Prediction and Site-Planning Software for Wireless Communication Systems. IEEE Antennas and Propagation Magazine, Vol. 49, No. 2, April 2007. [8] N. Cardona, P. Moller and F. Alonso (1995), Applicability of Walfisch-type urban propagation models. ELECTRONICS LETTERS 9th November 1995, Vol. 31 No. 23. [9] A. P. Garcia, H. Ortega, A. Navarro, H. Rodriguez(2003), Effect Of Terrain On Electromagnetic Propagation In Urban Environments On The Andean Region, Using The Cost 231-Walfisch-Ikegami Model And GIS Planning Tools Antennas and Propagation, Twelfth International Conference on (Conf. Publ. No. 491) Volume 1, 31 March-3 April 2003 Page(s):270 – 275. [10] Danilo Erricolo and Piergiorgio L. E. Uslenghi (2002). Propagation Path Loss—A Comparison between Ray-Tracing Approach and Empirical Models, IEEE Transactions On Antennas And Propagation, Vol. 50, No. 5, May 2002. [11] Dimitriou A.G, Sergiadis G.D (2006). Architectural features and urban propagation. Antennas and Propagation, IEEE Transactions on Volume 54, Issue 3? March 2006 Page(s):774 – 784. [12] Vieira, P. Queluz, P. Rodrigues (2007). A Dynamic Propagation Prediction Platform over Irregular Terrain and Buildings for Wireless Communications. IEEE 66th Vehicular Technology Conference. 30 Sept 2007-3 Oct 2007 Page(s):884 – 888 [13] Grosskopf R (2003). Field-strength prediction method for urban micro cells. 17th International Conference on Applied Electromagnetic and Communications. 1-3 Oct 2003. Page(s):213 – 216. [14] Nagy, L. Nagy, B (1994). Comparison and verification of urban propagation models Personal, Indoor and Mobile Radio Communications. 5th IEEE International Symposium on Wireless Networks - Catching the Mobile Future, 18-23 Sept 1994. Volume 4, Page(s):1359 – 1363.

Prediction of best coverage area for outdoor wireless propagation at 2.4 GHZ using Walfisch-Ikegami Simulation Model

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