Comparison of Empirical Propagation Path Loss Models for Mobile Communication

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dc.contributor.author Mollel, Michael S.
dc.contributor.author Michael, Kisangiri
dc.date.accessioned 2021-05-04T08:51:51Z
dc.date.available 2021-05-04T08:51:51Z
dc.date.issued 2014
dc.identifier.uri https://dspace.nm-aist.ac.tz/handle/20.500.12479/1167
dc.description This research article published by Computer Engineering and Intelligent Systems, Vol.5, No.9, 2014 en_US
dc.description.abstract Empirical propagation models have found favor in both research and industrial communities owing to their speed of execution and their limited reliance on detailed knowledge of the terrain. In mobile communication the accuracy prediction of path losses is a crucial element during network planning and optimization. However, the existence of multiple propagation models means that there is no propagation model which is precisely and accurate in prediction of path loss fit for every environs other than in which they were designed. This paper presents few empirical models suitable for path loss prediction in mobile communication. Experimental measurements of received power for the 900 MHz GSM system are made in urban, suburban, and rural areas of Dar es Salaam, Tanzania. Measured data are compared with those obtained by five prediction models: Stanford University Interim (SUI) models [1], the COST-231 Hata model [2], the ECC-33 model [3], the ERICSSON model [4], and the HATA-OKUMURA model [5]. The results show that in general the SUI, COST-231, ERICSSON, and Hata-Okumura under-predict the path loss in all environments, while the ECC-33 model shows the best results, especially in suburban and over-predict pathloss in urban area. en_US
dc.language.iso en en_US
dc.publisher Computer Engineering and Intelligent Systems en_US
dc.subject Propagation pathloss en_US
dc.subject Empirical models en_US
dc.subject Radio coverage en_US
dc.subject Mobile communications. en_US
dc.title Comparison of Empirical Propagation Path Loss Models for Mobile Communication en_US
dc.type Article en_US

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