A Multi-Wall Path Loss Model for Indoor UWB Propagation

Durantini, A.; Cassioli, Dajana

doi:10.1109/vetecs.2005.1543243

Cited by 23 publications

(17 citation statements)

References 13 publications

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“…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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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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show abstract

“…The λ-parameters found above leverage the occurrences of the three propagation mechanisms in (7). Now the same measured sample set of K M arrivals parameterized as (a k , α k , τ k ) used to estimate the λ D -parameter of the GTD-based model in III-A is used here to estimate rather the α-parameters of the proposed model.…”

Section: B Modeling the α-Parametersmentioning

confidence: 99%

“…Now the same measured sample set of K M arrivals parameterized as (a k , α k , τ k ) used to estimate the λ D -parameter of the GTD-based model in III-A is used here to estimate rather the α-parameters of the proposed model. The delay τ k of arrival k maps to the expected reconstructed order (µ l k , µ m k , µ n k ) through (10) and the expected reconstructed frequency parameter α k = α T · µ l k + α R · µ m k + α D · µ n k follows from (7). The values (α T , α R , α D ) can be found by minimizing the weighted meansquared error (see (6)) between the proposed model and the sample set…”

Section: B Modeling the α-Parametersmentioning

confidence: 99%

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A Frequency-Dependence Model for the Ultra-Wideband Channel Based on Propagation Events

Gentile

Kik

2007

IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference

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While the frequency-dependence of the wireless channel may be negligible for narrow to wideband signals, it has been shown that modeling this dependence for bandwidths in excess of 2 GHz improves channel reconstruction up to 40%. Yet to our knowledge, only Molisch et al. have done so for the ultra-wideband channel. Their benchmark frequency model however represents the average dependence over the collection of multi-path arrivals in the channel rather than that of individual arrivals. Building on the Geometric Theory of Diffraction, we propose a novel frequency model for individual arrivals according to the propagation events on their paths between the transmitter and receiver. We extract the model parameters from an extensive measurement campaign of 3000 channel frequency sweeps in three separate buildings combined with raytracing simulations, and show that ours fits the gathered data more closely than the benchmark model.

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“…Statistical (site-specific) one-slope models [15][16][17][18][19][20][21][22][23] (e.g. multi-wall models) predict path loss based on measurements of a specific site or for a specific environment.…”

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 Multi-Wall Path Loss Model for Indoor UWB Propagation

Cited by 23 publications

References 13 publications

Coverage prediction and optimization algorithms for indoor environments

Coverage prediction and optimization algorithms for indoor environments

A Frequency-Dependence Model for the Ultra-Wideband Channel Based on Propagation Events

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

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