Reflection model for calculation of the impulse response on IR‐wireless indoor channels using ray‐tracing algorithm

Pérez, Silvestre Rodríguez; Jiménez, Rafael Pérez; Hernández, Francisco J. López; González, O.; Alfonso, Alejandro José Ayala

doi:10.1002/mop.10159

Cited by 58 publications

(21 citation statements)

References 4 publications

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“…The propagation model is composed by the simulation algorithm and mathematical models used to describe the features of the elements of the optical link. To evaluate the impulse response of the VLC channel, a Monte Carlo ray-tracing algorithm has been adapted [10][11][12]. In general, the multi-wavelength impulse response for an arbitrary position of emitter E and receiver R can be expressed as an infinite sum of the form [9] h t; E; R; λ ð Þ¼h…”

Section: Propagation Modelmentioning

confidence: 99%

“…In this article, Phong's model has been used [11,13]. In contrast to Lambert's model, this model is able to approximate reflections consisting of both specular and diffusive components, which are described by…”

Section: Multiple-bounce Impulse Responsesmentioning

confidence: 99%

“…For this reason, in this article a tool for simulating the impulse response of indoor VLC channels using 3D computer-aided design (CAD) models is presented. The simulation tool is based on the Monte Carlo ray-tracing algorithm [10][11][12], and allows us to study the VLC signal propagation inside any simulation environment or 3D scene, regardless of its geometric shape, size (area), number of obstacles, etc. The tool features two fully differentiated parts.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of impulse response for indoor visible light communications using 3D CAD models

Rodriguez

Jiménez

Mendoza

et al. 2013

J Wireless Com Network

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In this article, a tool for simulating the channel impulse response for indoor visible light communications using 3D computer-aided design (CAD) models is presented. The simulation tool is based on a previous Monte Carlo ray-tracing algorithm for indoor infrared channel estimation, but including wavelength response evaluation. The 3D scene, or the simulation environment, can be defined using any CAD software in which the user specifies, in addition to the setting geometry, the reflection characteristics of the surface materials as well as the structures of the emitters and receivers involved in the simulation. Also, in an effort to improve the computational efficiency, two optimizations are proposed. The first one consists of dividing the setting into cubic regions of equal size, which offers a calculation improvement of approximately 50% compared to not dividing the 3D scene into sub-regions. The second one involves the parallelization of the simulation algorithm, which provides a computational speed-up proportional to the number of processors used.

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Section: Propagation Modelmentioning

confidence: 99%

Section: Multiple-bounce Impulse Responsesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of impulse response for indoor visible light communications using 3D CAD models

Rodriguez

Jiménez

Mendoza

et al. 2013

J Wireless Com Network

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“…In this work, Phong's model has been used [13]. In this model, the surface characteristics are defined by three parameters: the reflection coefficient ρ, the percentage of incident signal that is reflected diffusely r d and the directivity of the specular component of the reflection m. Therefore, the total received power in the kth time interval (width Δt) is calculated as the sum of the power of the N k rays that contribute in that interval.…”

Section: Channel Impulse Responsementioning

confidence: 99%

“…In order to estimate the impulse response in IR wireless indoor channels, several simulation methods have been put forth [10,11], but all of them share the same problem, namely, the intensive computational effort. However, we make use of a Monte Carlo ray-tracing algorithm [12,13], which presents a lower computational cost than previous methods, especially when a high temporal resolution and a large number of reflections are required. Indoor optical channel simulation can significantly enhance the design of angle diversity receivers but requires models that correctly fit the receiver characteristics and the remaining elements of the IR channel.…”

Section: Introductionmentioning

confidence: 99%

Design considerations of conventional angle diversity receivers for indoor optical wireless communications

Pérez

Mendoza

Jiménez

et al. 2013

J Wireless Com Network

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A conventional angle diversity receiver uses multiple receiving elements that are oriented in different directions, where each element employs its own filter and nonimaging concentrator, such as a compound parabolic concentrator (CPC) or hemispheric lens. In this paper, a study of the design of a conventional receiver structure using angle diversity that offers improved performance with respect to the infrared channel characteristics is presented. To this end, a recently proposed model for the effective signal collection area of a conventional angle diversity receiver that more closely approximates real behaviour than the ideal model is used. The inclusion of this model in a Monte Carlo ray-tracing algorithm allows us to investigate the effects of conventional receiver parameters on the main infrared channel parameters, such as path loss and rms delay spread. Furthermore, in order to determine the number of receiver elements, the outage probability and the average error probability are also considered. Based on the results, a conventional angle diversity receiver composed of seven elements is proposed, with one of them oriented towards the ceiling, and six angled at a 56°elevation with a 60°separation in azimuth. For each element, a CPC with a 50°field of view must be used.

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Multi‐GPU room response simulation with hardware raytracing

Thoman

Wippler

Hranitzky³

et al. 2021

Concurrency and Computation

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Time-of-flight camera systems are an essential component in 3D scene analysis and reconstruction for many modern computer vision applications. The development and validation of such systems require testing in a large variety of scenes and situations.Accurate room impulse response simulation greatly speeds up development and validation, as well as reducing its cost, but large computational overhead has so far limited its applicability. While the overall algorithmic requirements of this simulation differ significantly from 3D rendering, the recently introduced hardware raytracing support in GPUs nonetheless provides an interesting new implementation option. In this article, we present a new room response simulation method, implemented in a vendor-independent fashion with Vulkan compute shaders and leveraging NVIDIA VKRay hardware raytracing. We also extend this method to multi-GPU computation with asynchronous streaming and introduce a domain-specific high-performance compression scheme in order to overcome the limitations of on-board GPU memory and PCIe bandwidth when simulating very large scenes. Our implementation is, to the best of our knowledge, the first ever combined application of Vulkan hardware raytracing and multi-GPU compute in a non-rendering simulation setting. Compared to a state-of-the-art multicore CPU implementation running on 12 CPU cores, we achieve an overall speedup factor of up to 20 on a single consumer GPU, and 71 on four GPUs.

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Reflection model for calculation of the impulse response on IR‐wireless indoor channels using ray‐tracing algorithm

Abstract: On the other hand, with an optimal decision threshold and a w x Gaussian approximation, the BER in 2 is given as P Ž . BER s ⌽ y 8

Cited by 58 publications

References 4 publications

Simulation of impulse response for indoor visible light communications using 3D CAD models

Simulation of impulse response for indoor visible light communications using 3D CAD models

Design considerations of conventional angle diversity receivers for indoor optical wireless communications

Multi‐GPU room response simulation with hardware raytracing

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