A GPU-Based Radio Wave Propagation Prediction With Progressive Processing on Point Cloud

Pang, Min; Wang, Han; Lin, Kaiwei; Lin, Hai

doi:10.1109/lawp.2021.3072242

Cited by 6 publications

(2 citation statements)

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“…There are a handful of commercial radio wave propagation simulators based on the ray-optical approximation of wave propagation, e.g., [23], [24], that implement the approach for efficient propagation simulations. A method that has attracted recent interest is ray-tracing based on a laser-scanned point cloud of the environment [25]- [30]. Most published results of wave propagation simulations showcase either wholly outdoor or indoor simulations instead of the O2I case, given that obtaining a three-dimensional (3D) model of a building interior can be more difficult than using exteriors obtainable from e.g.…”

Section: Introductionmentioning

confidence: 99%

Wave Scatterer Localization in Outdoor-to-Indoor Channels at 4 and 14 GHz

Koivumaki

Karttunen

Haneda

2022

2022 16th European Conference on Antennas and Propagation (EuCAP)

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In this paper, we present the results of wave scatterer localization at 4.65 and 14.25 GHz in an outdoor-toindoor scenario at a traditional office building in Finland. The localization is based on a single-bounce model of interaction with a scatterer to localize the sources of measured multipath components. The estimated scatterer locations were mapped to an aerial photograph of the site and classified according to their location. We found that approximately two thirds of paths originate from higher order interactions with the environment. In contrast, one third of paths can be attributed to single-bounce interactions, with interior walls of the building being twice as strong sources of single-bounces as walls outside both in terms of power and number of paths.

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Section: Introductionmentioning

confidence: 99%

Wave Scatterer Localization in Outdoor-to-Indoor Channels at 4 and 14 GHz

Koivumaki

Karttunen

Haneda

2022

2022 16th European Conference on Antennas and Propagation (EuCAP)

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“…The maximum speed-up is obtained using RL approaches, intrinsically more efficient and more suitable than RT to parallelization. GPUbased approaches have been applied with success to propagation prediction in vehicular networks [22], drone-based applications [23,24], and indoor environments represented through point-cloud techniques [25,26].…”

Section: Introductionmentioning

confidence: 99%

Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications

Vitucci

Lu²,

Gordon

et al. 2021

Electronics

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In this work, the Discrete, Environment-Driven Ray Launching (DED-RL) algorithm, which makes use of parallelization on Graphic Processing Units, fully described in a previous paper, has been validated versus a large set of measurements to evaluate its performance in terms of both computational efficiency and accuracy. Three major urban areas have been considered, including a very challenging scenario in central San Francisco that was used as a benchmark to test an image-ray tracing algorithm in a previous work. Results show that DED-RL is as accurate as ray tracing, despite the much lower computation time, reduced by more than three orders of magnitude with respect to ray tracing. Moreover, the accuracy level only marginally depends on discretization pixel size, at least for the considered pixel size range. The unprecedented computational efficiency of DED-RL opens the way to numerous applications, ranging from RF coverage optimization of drone-aided cellular networks to efficient fingerprinting localization applications, as briefly discussed in the paper.

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