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支持无线传感器网络部署的城市车对基础设施环境中无线电传播信道的空间特性分析

Spatial Characterization of Radio Propagation Channel in Urban Vehicle-to-Infrastructure Environments to Support WSNs Deployment.

作者信息

Granda Fausto, Azpilicueta Leyre, Vargas-Rosales Cesar, Lopez-Iturri Peio, Aguirre Erik, Astrain Jose Javier, Villandangos Jesus, Falcone Francisco

机构信息

Electrical and Electronic Engineering Dept., Universidad de las Fuerzas Armadas ESPE, Sangolquí 171-5-231B, Ecuador.

School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico.

出版信息

Sensors (Basel). 2017 Jun 7;17(6):1313. doi: 10.3390/s17061313.

DOI:10.3390/s17061313
PMID:28590429
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5492156/
Abstract

Vehicular ad hoc Networks (VANETs) enable vehicles to communicate with each other as well as with roadside units (RSUs). Although there is a significant research effort in radio channel modeling focused on vehicle-to-vehicle (V2V), not much work has been done for vehicle-to-infrastructure (V2I) using 3D ray-tracing tools. This work evaluates some important parameters of a V2I wireless channel link such as large-scale path loss and multipath metrics in a typical urban scenario using a deterministic simulation model based on an in-house 3D Ray-Launching (3D-RL) algorithm at 5.9 GHz. Results show the high impact that the spatial distance; link frequency; placement of RSUs; and factors such as roundabout, geometry and relative position of the obstacles have in V2I propagation channel. A detailed spatial path loss characterization of the V2I channel along the streets and avenues is presented. The 3D-RL results show high accuracy when compared with measurements, and represent more reliably the propagation phenomena when compared with analytical path loss models. Performance metrics for a real test scenario implemented with a VANET wireless sensor network implemented ad-hoc are also described. These results constitute a starting point in the design phase of Wireless Sensor Networks (WSNs) radio-planning in the urban V2I deployment in terms of coverage.

摘要

车载自组织网络(VANETs)使车辆能够相互通信以及与路边单元(RSU)通信。尽管在专注于车对车(V2V)的无线信道建模方面有大量研究工作,但使用三维射线追踪工具进行车对基础设施(V2I)方面的工作做得并不多。这项工作使用基于内部三维射线发射(3D-RL)算法的确定性仿真模型,在5.9吉赫兹频率下评估典型城市场景中V2I无线信道链路的一些重要参数,如大尺度路径损耗和多径指标。结果表明,空间距离、链路频率、RSU的位置以及诸如环形交叉路口、障碍物的几何形状和相对位置等因素对V2I传播信道有很大影响。给出了沿街道和大道的V2I信道详细的空间路径损耗特性。与测量结果相比,3D-RL结果显示出高精度,并且与解析路径损耗模型相比,更可靠地表示了传播现象。还描述了使用自组织方式实现的VANET无线传感器网络对实际测试场景的性能指标。就覆盖范围而言,这些结果构成了城市V2I部署中无线传感器网络(WSNs)无线电规划设计阶段的起点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/c5b1f1b18f02/sensors-17-01313-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/5420db125611/sensors-17-01313-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/c96acc405a28/sensors-17-01313-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/144484b3e54a/sensors-17-01313-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/dbe4d9471bb3/sensors-17-01313-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/deae070022b7/sensors-17-01313-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/e43ac5411d40/sensors-17-01313-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/969450d74e18/sensors-17-01313-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/ae4e239f93f4/sensors-17-01313-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/b12b7a531514/sensors-17-01313-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/c5b1f1b18f02/sensors-17-01313-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/5420db125611/sensors-17-01313-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/c96acc405a28/sensors-17-01313-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/144484b3e54a/sensors-17-01313-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/dbe4d9471bb3/sensors-17-01313-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/deae070022b7/sensors-17-01313-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/e43ac5411d40/sensors-17-01313-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/969450d74e18/sensors-17-01313-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/ae4e239f93f4/sensors-17-01313-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/b12b7a531514/sensors-17-01313-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e26e/5492156/c5b1f1b18f02/sensors-17-01313-g010.jpg

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