Calibration of Ray-Tracing With Diffuse Scattering Against 28-GHz Directional Urban Channel Measurements

Charbonnier, Romain; Lai, Chieh-Ping; Tenoux, Thierry; Caudill, Derek; Gougeon, Gregory; Senic, Jelena; Gentile, Camillo; Corre, Yoann; Chuang, J.C.-I.; Golmie, Nada

doi:10.1109/tvt.2020.3038620

Cited by 19 publications

(4 citation statements)

References 54 publications

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“…However, NLoS contributions on single or multiple bounces e.g., on metal objects are not with diffused rays [25]. The diffused rays are generated by using a stochastic model and rely on the surface roughness on which rays reflect and are clustered around the main reflected component [22,26].…”

Section: A Literature Reviewmentioning

confidence: 99%

Methods for Simplifying Quasi-Deterministic Millimeter-Wave Channel Models

et al. 2023

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It is crucial to have a dependable and precise channel model in order to study the properties of millimeter wave (mmWave) propagation. The Quasi-deterministic (QD) channel model is employed in this viewpoint, which describes the propagation of mm-Wave as a group of reflected and scattered rays originating from a complex environmental setup. These rays are assumed to travel in clusters, with each cluster consisting of a deterministic ray followed by postcursor rays and preceded by precursor rays. The summation of these rays is the number of multiple path components (MPCs) of each cluster. However, this comes at the cost of higher computational complexity for the channel model, which can hinder the simulation's scalability. To simplify the QD channel model while maintaining accuracy, one option is to decrease the number of MPCs. In this paper, we present an analysis of path gains (PGs) of specular and diffused rays to reduce the total number of MPCs. Specifically, we propose two different reduction methods namely: i) the reduced post rays (RPR) method ii) the removed surfaces post rays (RSPR) method. The computational performance of the proposed methods is investigated in terms of computational time, and complexity. Additionally, the accuracy validation compared to the original QD model is evaluated in terms of PG cumulative distribution function (CDF), signal-to-noise ratio (SNR), and intra-cluster statistics. The proposed methods' complexity and accuracy were assessed by examining measured data from indoor and outdoor environments at 60 GHz and 28 GHz, respectively. Both first and second-order reflection orders were tested to illustrate the balance between the two variables. The simplified methods suggested can decrease computational time by approximately 16% and 11% for RSPR and RPR schemes, respectively, when compared to the original QD.INDEX TERMS Diffused rays, Millimeter wave propagation, Multipath components, Path gain, Quasideterministic channel models, intra-cluster statistics, Ray tracer.

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Section: A Literature Reviewmentioning

confidence: 99%

Methods for Simplifying Quasi-Deterministic Millimeter-Wave Channel Models

et al. 2023

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“…Simple ray-tracing-based PL prediction, which includes up to 10 reflections from the wall and the ground but ignores street-clutter, has predicted stronger signal power than in free space [4], [8], which itself is 10 dB hotter than the measured data in urban canyons [8]. Several works [32]- [34] have focused on (3D) ray-tracing-based prediction for mm-wave in urban street canyons, where the accuracy of ray-tracing is shown to be strongly dependent on modeled wave propagation mechanisms [33] and environmental feature description [34]. Therefore, an in-depth proper investigation of street clutter approximation and modeling is needed to bring ray-tracing prediction up to a reasonable level of accuracy.…”

Section: Performance Evaluation and Analysismentioning

confidence: 99%

Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements

Gupta,

Du,

Chizhik

et al. 2022

Preprint

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Large bandwidth at mm-wave is crucial for 5G and beyond but the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen in urban canyons, whereas ray-tracing, capable of characterizing site-specific features, faces challenges in describing foliage and street clutter and associated reflection/diffraction ray calculation. Machine learning (ML) is promising but faces three key challenges in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to new streets; 3) overwhelmingly complex features/models. We propose an ML-based urban canyon PL prediction model based on extensive 28 GHz measurements from Manhattan where street clutters are modeled via a LiDAR point cloud dataset and buildings by a mesh-grid building dataset. We extract expert knowledge-driven street clutter features from the point cloud and aggressively compress 3D-building information using convolutional-autoencoder. Using a new street-bystreet training and testing procedure to improve generalizability, the proposed model using both clutter and building features achieves a prediction error (RMSE) of 4.8 ± 1.1 dB compared to 10.6±4.4 dB and 6.5±2.0 dB for 3GPP LOS and slope-intercept prediction, respectively, where the standard deviation indicates street-by-street variation. By only using four most influential clutter features, RMSE of 5.5 ± 1.1 dB is achieved.

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“…In our network planning tool, the transmitted signal is represented by a set of planar wavefronts where the attenuation is based on the channel model and the angular information is based on the room geometry and locations of the transmitter and receiver. Ray launching algorithms for determining channel characteristics and predicting network performance at mmWave frequencies are investigated in multiple papers [41][42][43][44][45]. Most ray launching implementations consider transmission, specular reflection, and diffraction.…”

Section: Introductionmentioning

confidence: 99%

“…Most ray launching implementations consider transmission, specular reflection, and diffraction. Even though diffuse scattering accounts for up to 26% of the total received power [46], it is not considered in most ray launching implementations [41].…”

Section: Introductionmentioning

confidence: 99%

Directive mmWave radio channel modeling in a ship hull

Beelde

Lopéz

David

et al. 2021

Int. J. Microw. Wireless Technol.

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Wireless connectivity has been realized for multiple environments and different frequency bands. However, little research exists about mmWave communication in industrial environments. This paper presents the 60 GHz double-directional radio channel for mmWave communication in a ship hull for Line-of-Sight (LOS) and non-Line-of-Sight (NLOS) conditions. We performed channel measurements using the Terragraph channel sounder at different locations in the ship hull and fitted LOS path loss to a one-slope path loss model. Path loss and root-mean-square delay spread of the LOS path is compared to the reflected path with lowest path loss. NLOS communication via this first-order reflected path is modeled by calculating the path distance and determining the reflection loss. The reflection losses have a considerable contribution to the signal attenuation of the reflected path. The channel models are implemented in an indoor coverage prediction tool, which was extended with a ray launching algorithm and validated by comparison with an analytical electromagnetic solver. The results show that the mmWave radio channel allows high-throughput communication within a ship hull compartment, even when no LOS path between the transmitter and receiver is present.

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Calibration of Ray-Tracing With Diffuse Scattering Against 28-GHz Directional Urban Channel Measurements

Cited by 19 publications

References 54 publications

Methods for Simplifying Quasi-Deterministic Millimeter-Wave Channel Models

Methods for Simplifying Quasi-Deterministic Millimeter-Wave Channel Models

Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements

Directive mmWave radio channel modeling in a ship hull

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