Ray tracing-based radio propagation modeling for indoor localization purposes

Laaraiedh, Mohamed; Uguen, Bernard; Stephan, Julien; Corre, Yoann; Lostanlen, Yves; Raspopoulos, Marios; Stavrou, Stavros

doi:10.1109/camad.2012.6335350

Cited by 14 publications

(11 citation statements)

References 11 publications

(14 reference statements)

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“…RT models have been recently used to derive millimeter‐wave path loss models [ Jacob et al , ; Ghaddar et al , ] to perform multidimensional channel characterization, often in combination with measurements [ Peter et al , ; Dupleich et al , ; Gustafson et al , ; Kazemi et al , ; Rasekh et al , ], or to design the radio interface of 5G mobile radio systems [ Larew et al , ; Fugen et al , ]. Other recent applications include the use of RT to characterize the THz propagation channel [ Priebe et al , ; Fugen et al , ] or to assist indoor localization techniques [ Laaraiedh et al , ].…”

Section: Introductionmentioning

confidence: 99%

Ray tracing propagation modeling for future small-cell and indoor applications: A review of current techniques

et al. 2015

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Applied for the first time to mobile radio propagation modeling at the beginning of the nineties, ray tracing is now living a second youth. It is probably the best model to assist in the design and planning of future short-range, millimeter-wave wireless systems, where the more limited propagation environment with respect to UHF frequencies allows to overcome traditional high-CPU time limitations while the higher operating frequency makes ray-optics approximations less drastic and allows to achieve an unprecedented level of accuracy. An overview of ray tracing propagation modeling is given in this paper, with a special attention to future prospects and applications. In particular, frontiers of ray-based propagation modeling such as extension to diffuse scattering, multidimensional channel characterization, multiple-input multiple-output (MIMO) capacity assessments, and future applications such as real-time ray tracing are addressed in the paper with reference to the work recently carried out at the University of Bologna.

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Section: Introductionmentioning

confidence: 99%

Ray tracing propagation modeling for future small-cell and indoor applications: A review of current techniques

et al. 2015

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“…Data-augmentation approaches. One more way to build or complete radio databases relies on deterministic simulation means, such as Ray-Tracing tools (e.g., [16,25,31]). The latter aim at predicting in-site radio propagation (i.e., simulating electromagnetic interactions of transmitted radio waves within an environment).…”

Section: Interpolation Techniquesmentioning

confidence: 99%

“…Data augmentation techniques have thus been proposed for artificially increasing the number of measurements in such radio map reconstruction problems. Typically, once calibrated over a few real field measurements, deterministic prediction tools can generally simulate electromagnetic interactions of transmitted radio waves within the environment of study [16,25,31]. The purpose is then to use the generated synthetic data as additional data to train complex models for map interpolation.…”

Section: Introductionmentioning

confidence: 99%

Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search

Malkova¹,

Pauletto²,

Villien³

et al. 2021

Preprint

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In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given (RSS) map. These ground-truth measurements along with the predictions of the model over a set of randomly chosen points are then used to train a second NN model having the same architecture. Experimental results show that signal predictions of this second model outperforms non-learning based interpolation stateof-the-art techniques and NN models with no architecture search on five large-scale maps of RSS measurements.

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“…Indeed, WLAN technologies are good candidates for indoor applications. The indoor channel modeling is usually performed through predictions based on ray-tracing methods [12], [13]. These techniques required the knowledge of the electromagnetic properties of the building materials.…”

Section: Measurement Setup and Specimensmentioning

confidence: 99%

“…An application devoted to the characterization of building materials at 2.45 and 5 GHz based on a low-cost instrumentation is presented to situate the performances of the technique proposed. Indeed, the knowledge of the dielectric parameters of building materials is required for the deployment and optimization of wireless local area networks (WLANs) [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Geometrical Optics-Based Model for Dielectric Constant and Loss Tangent Free-Space Measurement

Haddadi

Lasri

2014

IEEE Trans. Instrum. Meas.

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A microwave free-space reflection method for determining the complex permittivity of planar dielectric materials is demonstrated. The method makes use of the measurement of the near-field microwave reflection coefficient of a metal-backed sample. The modeling of the structure and its calibration are based on geometrical optics considering spherical electromagnetic waves propagating through the material. The technique that presents a number of features such as low-cost, compactness, robustness, and reliability is a good candidate for industrial applications. As a demonstration, dielectric parameters extraction of building materials is experimentally demonstrated for wireless local area network operations in the 2.45-and 5-GHz bands.

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Ray tracing-based radio propagation modeling for indoor localization purposes

Cited by 14 publications

References 11 publications

Ray tracing propagation modeling for future small-cell and indoor applications: A review of current techniques

Ray tracing propagation modeling for future small-cell and indoor applications: A review of current techniques

Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search

Geometrical Optics-Based Model for Dielectric Constant and Loss Tangent Free-Space Measurement

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