A theoretical model for radio signal attenuation inside buildings

Whitman, G. M.; Kim, Kyu-Sung; Niver, Edip

doi:10.1109/25.406630

Cited by 18 publications

(17 citation statements)

References 21 publications

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“…In the last decades, significant effort has been focused on the characterization of the indoor channel for narrowband systems. Statistical (Motley & Keenan, 1990;Saleh & Valenzuela, 1987;Seidel & Rappaport, 1992;Tornevik et al, 1993) and deterministic (Lauer et al, 1984;Saez de Adana et al, 2000;Tarng et al, 1997;Whitman et al, 1995) models have been used most frequently in these studies. The statistical models are based on the obtention of closed formulas to characterize the propagation channel.…”

Section: Introductionmentioning

confidence: 99%

Propagation Models for the Characterization of the Indoor UWB Channel

Adana¹

2011

Ultra Wideband Communications: Novel Trends - Antennas and Propagation

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Section: Introductionmentioning

confidence: 99%

Propagation Models for the Characterization of the Indoor UWB Channel

Adana¹

2011

Ultra Wideband Communications: Novel Trends - Antennas and Propagation

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“…A theoretical waveguide model permitting a rigorous modal solution is proposed for predicting path loss inside buildings in [6].…”

Section: Related Studymentioning

confidence: 99%

“…These studies describe either ray models [1][2][3][4][5], numerical solver models [6][7][8][9], heuristic predictions [10][11][12][13], statistical (site-specific) models [14][15][16][17][18][19][20][21][22], or specific propagation aspects [23][24][25]. Our algorithm can be classified as heuristic.…”

Section: Related Studymentioning

confidence: 99%

Coverage prediction and optimization algorithms for indoor environments

David

Joseph

Vanhecke

et al. 2012

J Wireless Com Network

160

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A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types.

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“…Also, the dielectric material properties of the environment have to be accurately known to obtain a good prediction. A theoretical waveguide model permitting a rigorous modal solution is proposed for predicting path loss inside buildings in [7].…”

Section: Introductionmentioning

confidence: 99%

“…Numerical solver model s [7][8][9][10] consist of screen or integral methods, Finitedifference time-domain (FDTD),. .…”

Section: Introductionmentioning

confidence: 99%

Simple Indoor Path Loss Prediction Algorithm and Validation in Living Lab Setting

David

Joseph

Vanhecke

et al. 2011

Wireless Pers Commun

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A simple heuristic algorithm has been developed for an accurate prediction of indoor wireless coverage, aiming to improve existing models upon multiple aspects. Extensive measurements on several floors in four buildings are used as validation cases and show an excellent agreement with the predictions. As the prediction is based on the free-space loss model for every environment, it is generally applicable, while other propagation models are often too dependent on the environment upon which it is based. The applicability of the algorithm to a wireless testbed network in a living lab setting with WLAN 802.11b/g nodes is investigated by a site survey. The results can be extremely useful for the rollout of indoor wireless networks.

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A theoretical model for radio signal attenuation inside buildings

Cited by 18 publications

References 21 publications

Propagation Models for the Characterization of the Indoor UWB Channel

Propagation Models for the Characterization of the Indoor UWB Channel

Coverage prediction and optimization algorithms for indoor environments

Simple Indoor Path Loss Prediction Algorithm and Validation in Living Lab Setting

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