Increased reliability of outdoor millimeter-wave link simulations by leveraging lidar point cloud

Stephan, Julien; Corre, Yoann; Charbonnier, Romain; Lostalen, Yves

doi:10.1049/cp.2018.0383

Cited by 12 publications

(5 citation statements)

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“…Järveläinen et al [30] also proposed a point cloud-based ray-tracing simulation method to estimate the indoor propagation of mmWave channels. Stéphan et al [31] proposed utilizing accurate geographical data such as point clouds acquired by LiDAR to increase the reliability of mmWave link simulations in outdoor environments.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud-Based Proactive Link Quality Prediction for Millimeter-Wave Communications

Ohta,

Nishio,

Kudo

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Previous studies have proposed machine learning-based methods to predict received signal strength for future time periods using time series of depth images to mitigate the line-of-sight (LOS) path blockage by pedestrians in mmWave communication. However, these image-based methods have limited applicability due to privacy concerns as camera images may contain sensitive information. This study proposes a point cloud-based method for mmWave link quality prediction and demonstrates its feasibility through experiments. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts realistic indoor experiments, where the link quality fluctuates significantly due to human blockage, using commercially available IEEE 802.11ad-based 60 GHz wireless LAN devices and Kinect v2 RGB-D camera and Velodyne VLP-16 light detection and ranging (LiDAR) for point cloud acquisition. The experimental results showed that our proposed method can predict future large attenuation of mmWave received signal strength and throughput induced by the LOS path blockage by pedestrians with comparable or superior accuracy to image-based prediction methods. Hence, our point cloud-based method can serve as a viable alternative to image-based methods.INDEX TERMS LiDAR, link quality prediction, machine learning, millimeter-wave communication, point cloud VOLUME , 1This article has been accepted for publication in IEEE Transactions on Machine Learning in Communications and Networking. This is the author's version which has not been fully edited an content may change prior to final publication.

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Section: Related Workmentioning

confidence: 99%

Point Cloud-Based Proactive Link Quality Prediction for Millimeter-Wave Communications

Ohta,

Nishio,

Kudo

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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“…The use of point clouds for quasi-deterministic channel modeling has been discussed to aim at more accurate site-specific channel modeling [87][88][89]. Point clouds are obtained by optical measurements of a site, e.g., by cameras and laser.…”

Section: The Use Of Point Cloud Models Of the Environmentmentioning

confidence: 99%

Standardization of Propagation Models for Terrestrial Cellular Systems: A Historical Perspective

Tataria

Haneda

Molisch

et al. 2020

Int J Wireless Inf Networks

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Propagation models constitute a fundamental building block of wireless communications research. Before we build and operate real systems, we must understand the science of radio propagation, and develop channel models that both reflect the important propagation processes and allow a fair comparison of different systems. In the past five decades, wireless systems have gone through five generations, from supporting voice applications to enhanced mobile broadband. To meet the ever increasing data rate demands of wireless systems, frequency bands covering a wide range from 800 MHz to 100 GHz have been allocated for use. The standardization of these systems started in the early/mid 1980s in Europe by the European Telecommunications Standards Institute with the advent of Global System for Mobile Communications. This motivated the development of the first standardized propagation model by the European Cooperation in Science and Technology (COST) 207 working group. These standardization activities were continued and expanded for the third, fourth, and fifth generations of COST, as well as by the Third Generation Partnership Project, and the International Telecommnunication Union. This paper presents a historical overview of the standardized propagation models covering first to fifth-generation systems. In particular, we discuss the evolution and standardization of pathloss models, as well as large and small-scale fading parameters for single antenna and multiple antenna systems. Furthermore, we present insights into the progress of deterministic modelling across the five generations of systems, as well as discuss more advanced modelling components needed for the detailed simulations of millimeter-wave channels. A comprehensive bibliography at the end of the paper will aid the interested reader to dig deeper.

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“…They combine ray‐tracing method based on computer‐aided design (CAD) models of buildings in a given area with scattering method based on detailed point clouds of the same area. Similarly, Stéphan, Corre, Charbonnier, and Lostalen (2018) investigates the benefit of exploiting point cloud data in millimeter‐wave radio propagation models, which are used for designing in‐street radio links for next‐generation wireless networks. The ability of point clouds to capture details, such as the structure, allows more reliable estimates of signal attenuation.…”

Section: Related Workmentioning

confidence: 99%

Radio propagation models for differential GNSS based on dense point clouds

Kubelka

Dandurand

Babin

et al. 2020

Journal of Field Robotics

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Accurate geolocation of mobile equipment operating in outdoor environments is an increasingly important question in robotics and automation. Modern geolocation systems, however, rely on the crucial ability for a mobile device to receive specific radio signals at all times. As such geolocation systems are increasingly deployed in harsh or difficult environments, for example, in the presence of tall buildings or dense forest, it becomes critical to predict how the environment will impact the propagation of these radio signals. To this effect, we present a new signal propagation model that can determine what areas would be favorable for global navigation satellite system (GNSS) positioning, based on a prior three-dimensional (3D) point cloud map of the environment. Our model can predict both the number of usable satellites for a GNSS receiver and the strength of the reference radio signal used in the differential GNSS scenario. Contrary to others, it takes into account both signal occlusion and absorption mechanisms, given the geometry and density of the point cloud map. We designed two rugged mobile data-collecting platforms, both to generate the 3D maps of the environment, as well as to gather various ground truth for GNSS satellite and local radio signals. Environments used for our field deployments included a boreal forest, a subarctic forest and diverse industrial areas. Experimental results indicate that our model performs well in both structured and unstructured environments, with median errors of 1.10 for the predicted number of satellites and 1.85 dBm for the strength of the differential GNSS correction signals.

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Increased reliability of outdoor millimeter-wave link simulations by leveraging lidar point cloud

Cited by 12 publications

References 0 publications

Point Cloud-Based Proactive Link Quality Prediction for Millimeter-Wave Communications

Point Cloud-Based Proactive Link Quality Prediction for Millimeter-Wave Communications

Standardization of Propagation Models for Terrestrial Cellular Systems: A Historical Perspective

Radio propagation models for differential GNSS based on dense point clouds

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