Measurements and modeling of human blockage effects for multiple millimeter Wave bands

Qi, Wenzhe; Huang, Jie; Sun, Jian; Tan, Yi; Wang, Cheng-Xiang; Ge, Xiaohu

doi:10.1109/iwcmc.2017.7986524

Cited by 23 publications

(18 citation statements)

References 13 publications

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“…The detailed mathematical description of these models is given in the Appendix. A similar survey comparing some popular human blockage models is available in [9], but covers only the distance-dependent human shadowing for frequencies up to 30 GHz. Hence, we complement this study by providing further insights into the body orientation and antenna height dependency of human blockage, which are the essential features when applying the model for cellular mobile access links.…”

Section: Human Blockage Modelsmentioning

confidence: 99%

“…Finally, R ⊥ 0 and R ⊥ n are the polarimetric Fresnel reflection coefficients of a plane wave at 0-and n-face, where incident and reflecting angles are given by φ and nπ − φ, respectively. The possible singularity of the cotangent functions in (9) around the reflection and shadowing boundaries is mitigated through the approximation…”

Section: Conducting Screen and Wedge Modelsmentioning

confidence: 99%

“…Table 1 lists some typical values of the complex permittivity, ϵ c = ϵ ′ − jϵ ′′ , at 60 GHz, for human skin, fat, muscle and of pure water at 20 • C. We note that the dielectric properties of pure water at 20 • the estimation of diffracted field from right-angle wedges, [30] proposes a different approximation of the diffraction coefficients using inverse problem theory. It is noteworthy that the wedge diffraction becomes a thin screen when n = 2, allowing us to calculate blockage loss due to finitely conducting screens using (9).…”

Section: Dielectric Propertiesmentioning

confidence: 99%

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Modeling Human Blockage at 5G Millimeter-Wave Frequencies

Virk

Haneda

2020

IEEE Trans. Antennas Propagat.

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Millimeter-wave (mm-wave) spectrum unravels the humongous and accelerating demand for wireless data rates and, therefore, it will be a fundamental ingredient of the fifthgeneration (5G) wireless technology. In case of mm-wave access links, humans are the most noticeable blockers of electromagnetic waves from access points to mobile stations and hence cause temporal variation in the radio channel. This paper presents human blockage measurements in the anechoic chamber at 15, 28 and 60 GHz frequencies employing 15 human subjects of different sizes and weights. An effective three-dimensional human blockage model as a double-truncated and absorbing multiple knife-edge (DTMKE) scheme is also proposed. By calculating diffraction from the DTMKE, the frequency, body orientation and antenna height dependency of the blockage are most accurately reproduced compared to the existing models, such as absorbing double knife-edge model and third generation partnership project (3GPP) human blockage model. The results demonstrate that the losses are proportional to the cross-section of the human body with respect to the radio link. Furthermore, the blockage loss decreases as the height of the transmitting antenna increases.Index Terms-Millimeter-wave (mm-wave), fifth-generation (5G), three-dimensional (3D), human blockage.

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Section: Human Blockage Modelsmentioning

confidence: 99%

Section: Conducting Screen and Wedge Modelsmentioning

confidence: 99%

Section: Dielectric Propertiesmentioning

confidence: 99%

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Modeling Human Blockage at 5G Millimeter-Wave Frequencies

Virk

Haneda

2020

IEEE Trans. Antennas Propagat.

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“…This paper presents a machine learning based approach to support modelling of diffraction loss around a human body at mmWave frequencies. Models such as the double knife edge [13], geometric theory of diffraction [14] or more recent work by the authors on the shield edge [15] could be equally applied to obtain a predicted diffraction loss based on the width of the body. However, this will only model one part of the loss around a body that can be found deterministically.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Approach for Diffraction Loss Variation Prediction by the Human Body

Khalily

Brown

Tafazolli

2019

Antennas Wirel. Propag. Lett.

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This paper presents a machine learning (ML) based model to predict the diffraction loss around the human body. Practically, it is not reasonable to measure the diffraction loss changes for all possible body rotation angles, builds and line of sight (LoS) elevation angles. A diffraction loss variation prediction model based on a non-parametric learning technique called Gaussian process (GP) is introduced. Analysed results state that 86% correlation and normalised mean square error (NMSE) of 0.3 on the test data is achieved using only 40% of measured data. This allows a 60% reduction in required measurements in order to achieve a well-fitted ML loss prediction model. It also confirms the model generalizability for non-measured rotation angles.Index Terms-Diffraction loss, guassian process (GP), machine learning (ML), network planning tool.

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“…As an example, blockage can be modeled based on 2-D knife edge diffraction[56]. This allows the receiver to apply a transient change detection algorithm based on the mean change of a complex Gaussian distribution as the signal integrity metric for the LOS blockage identification[57].September 18, 2018 DRAFT…”

mentioning

confidence: 99%

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Shahmansoori

Uguen

Destino

et al. 2019

IEEE Signal Process. Lett.

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Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and timeof-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase.

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Measurements and modeling of human blockage effects for multiple millimeter Wave bands

Cited by 23 publications

References 13 publications

Modeling Human Blockage at 5G Millimeter-Wave Frequencies

Modeling Human Blockage at 5G Millimeter-Wave Frequencies

Machine-Learning-Based Approach for Diffraction Loss Variation Prediction by the Human Body

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

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