Outdoor and indoor channel characterization by a 3D simulation software

Escarieu, Frédéric; Pousset, Yannis; Aveneau, Lilian; Vauzelle, Rodolphe

doi:10.1109/pimrc.2001.965441

Cited by 28 publications

(15 citation statements)

References 3 publications

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“…The shape parameters σ, µ, Ω, k, λ in (10) are all set to obtain a mean of 73 and a standard deviation of 1.2, the resulting values are given in Table 1(a). We generate 30 samples of sizes n ranging from 100 to 3000 of the laws in model (10). On each of those sample, we apply the three recognition methods discussed earlier: optimal histograms (6), empirical histogram (7) and KS distance (9).…”

Section: Application In a Theoretical Setupmentioning

confidence: 99%

“…Laboratory SIC-XLIM developped a software allowing to simulate the fast fading behavior of a radio propagation channel in various environments, see [10]. From this software, we collected n = 700 data representing the attenuation (dB) of the signal in both Line Of Sight (LOS) and Non Line Of Sight (NLOS) configurations, see figure 2.…”

Section: Application In a Real Setupmentioning

confidence: 99%

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Law recognition via histogram-based estimation

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Alata

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we study the problem of recognizing an unknown probability density function from one of its sample which is of interest in signal and image processing or telecommunication applications. By opposition with the classical Kolmogorov-Smirnov method based on empirical cumulative functions, we consider histogram estimators of the density itself built from our data. Those histograms are generated via model selection, more specifically via a codelength-based Information Criterion. From the histograms, we may compute a Kullback-Leibler distance to any theoretical law which is used to complete the recognition. We apply this histogrambased method for law recognition in a theoretical setup where the true density is known as well as in a real setup where data come from radio channel propagation experimentation.

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Section: Application In a Theoretical Setupmentioning

confidence: 99%

Section: Application In a Real Setupmentioning

confidence: 99%

Law recognition via histogram-based estimation

Coq

Alata

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The classical computation techniques of such zones use electromagnetic wave propagation models, which can be either vectorial [Athanasiadou and Nix, 2000;Escarieu et al, 2001] or scalar [Deygout, 1991]. Such models are applied to a set of fictive reception points, distributed with a constant spatial step in the studied geographical zone.…”

Section: Introductionmentioning

confidence: 99%

An optimization in computation time for the prediction of radio coverage zones

et al. 2007

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[1] This paper presents a method which optimizes the computation time for radio coverage prediction, whatever propagation model is used. The principle consists in reducing, in comparison with classical techniques, the number of application points of a propagation model. The proposed method is based on a multiresolution analysis of measured signals operating at 1.8 GHz and on an electromagnetic analysis of the propagation environment. The method is compared with a classical technique and is evaluated in terms of a reduction in computation time and of an increase in accuracy. Satisfactory results were obtained for microcells and small cells, and gains in computation time, with values close to 3 and 80, were achieved for scalar and vectorial models, respectively. As 90% of the estimation errors relating to the received signal level are less than 2.2 dB, a high level of accuracy was also assured.

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“…Thus, the aim of this study is also to find the simplest environment modelling that can ensure a satisfactory level of accuracy while not involving excessive computation time. In this article, we consider a deterministic model based on an optimised 3D deterministic ray-tracing method [8,9] associated with characterisation software [10]. In order to provide statistically robust results, a large number of simulations are undertaken, the approach being considered as a semi-deterministic MIMO characterisation.…”

Section: Introductionmentioning

confidence: 99%