• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

城市交叉口车辆多链路通信的路径损耗和阴影衰落模型。

A Path Loss and Shadowing Model for Multilink Vehicle-to-Vehicle Channels in Urban Intersections.

机构信息

Deptartment of Electrical and Information Technology, Lund University, Box 118, SE-221 00 Lund, Sweden.

Volvo Car Corporation, SE-405 31 Göteborg, Sweden.

出版信息

Sensors (Basel). 2018 Dec 14;18(12):4433. doi: 10.3390/s18124433.

DOI:10.3390/s18124433
PMID:30558221
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308981/
Abstract

The non line-of-sight (NLOS) scenario in urban intersections is critical in terms of traffic safety-a scenario where Vehicle-to-Vehicle (V2V) communication really can make a difference by enabling communication and detection of vehicles around building corners. A few NLOS V2V channel models exist in the literature but they all have some form of limitation, and therefore further research is need. In this paper, we present an alternative NLOS path loss model based on analysis from measured V2V communication channels at 5.9 GHz between six vehicles in two urban intersections. We analyze the auto-correlation of the large scale fading process and the influence of the path loss model on this. In cases where a proper model for the path loss and the antenna pattern is included, the de-correlation distance for the auto-correlation is as low as 2⁻4 m, and the cross-correlation for the large scale fading between different links can be neglected. Otherwise, the de-correlation distance has to be much longer and the cross-correlation between the different communication links needs to be considered separately, causing the computational complexity to be unnecessarily large. With these findings, we stress that vehicular ad-hoc network (VANET) simulations should be based on the current geometry, i.e., a proper path loss model should be applied depending on whether the V2V communication is blocked or not by other vehicles or buildings.

摘要

非视距 (NLOS) 场景在城市交叉口对于交通安全至关重要——在这种情况下,车对车 (V2V) 通信可以通过实现建筑物拐角处车辆的通信和检测来真正发挥作用。文献中存在一些 NLOS V2V 信道模型,但它们都存在某种形式的限制,因此需要进一步研究。在本文中,我们提出了一种基于在两个城市交叉口的 5.9GHz 频段的六辆车之间的 V2V 通信信道测量结果的替代 NLOS 路径损耗模型。我们分析了大尺度衰落过程的自相关特性以及路径损耗模型对其的影响。在包含适当的路径损耗和天线方向图模型的情况下,自相关的去相关距离低至 2⁻4 米,不同链路之间的大尺度衰落的互相关可以忽略不计。否则,去相关距离必须更长,并且需要分别考虑不同通信链路之间的互相关,这会导致不必要的计算复杂度增加。有了这些发现,我们强调车对自组网 (VANET) 模拟应该基于当前的几何形状,即应该根据 V2V 通信是否被其他车辆或建筑物阻挡来应用适当的路径损耗模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/7cb64e6836b8/sensors-18-04433-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/db34da7ef480/sensors-18-04433-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/539357782d7b/sensors-18-04433-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/144094b79b37/sensors-18-04433-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/5373c282779c/sensors-18-04433-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/c713f2f04df2/sensors-18-04433-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/1626592c61f9/sensors-18-04433-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/a57e47dbaa0c/sensors-18-04433-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/7d77c45be1ca/sensors-18-04433-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/4bf4518aac40/sensors-18-04433-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/1ef6782318c7/sensors-18-04433-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/1eb995261465/sensors-18-04433-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/47561eb7bf14/sensors-18-04433-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/7cb64e6836b8/sensors-18-04433-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/db34da7ef480/sensors-18-04433-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/539357782d7b/sensors-18-04433-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/144094b79b37/sensors-18-04433-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/5373c282779c/sensors-18-04433-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/c713f2f04df2/sensors-18-04433-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/1626592c61f9/sensors-18-04433-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/a57e47dbaa0c/sensors-18-04433-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/7d77c45be1ca/sensors-18-04433-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/4bf4518aac40/sensors-18-04433-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/1ef6782318c7/sensors-18-04433-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/1eb995261465/sensors-18-04433-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/47561eb7bf14/sensors-18-04433-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/610b/6308981/7cb64e6836b8/sensors-18-04433-g013.jpg

相似文献

1
A Path Loss and Shadowing Model for Multilink Vehicle-to-Vehicle Channels in Urban Intersections.城市交叉口车辆多链路通信的路径损耗和阴影衰落模型。
Sensors (Basel). 2018 Dec 14;18(12):4433. doi: 10.3390/s18124433.
2
Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections.基于神经进化的自适应天线阵波束形成方案,提高交叉口 V2V 通信性能。
Sensors (Basel). 2021 Apr 23;21(9):2956. doi: 10.3390/s21092956.
3
Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities.智能城市中智能车辆通信的确定性传播建模。
Sensors (Basel). 2018 Jul 3;18(7):2133. doi: 10.3390/s18072133.
4
Spatial Characterization of Radio Propagation Channel in Urban Vehicle-to-Infrastructure Environments to Support WSNs Deployment.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
Sensors (Basel). 2017 Jun 7;17(6):1313. doi: 10.3390/s17061313.
5
Novel Road Traffic Management Strategy for Rapid Clarification of the Emergency Vehicle Route Based on V2V Communications.基于车对车通信的新型道路交通管理策略,用于快速明确紧急车辆路径。
Sensors (Basel). 2021 Jul 28;21(15):5120. doi: 10.3390/s21155120.
6
Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios.多车车对车场景下 5.9GHz 信道非平稳性分析。
Sensors (Basel). 2021 May 23;21(11):3626. doi: 10.3390/s21113626.
7
Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.用于在城市和郊区环境中集成车辆传感应用的双斜率路径损耗模型。
Sensors (Basel). 2024 Jul 4;24(13):4334. doi: 10.3390/s24134334.
8
A Novel Method to Enable the Awareness Ability of Non-V2V-Equipped Vehicles in Vehicular Networks.一种使车载网络中未配备车对车(V2V)功能的车辆具备感知能力的新方法。
Sensors (Basel). 2019 May 11;19(9):2187. doi: 10.3390/s19092187.
9
Development and application of an aerosol screening model for size-resolved urban aerosols.用于粒径分辨的城市气溶胶的气溶胶筛选模型的开发与应用。
Res Rep Health Eff Inst. 2014 Jun(179):3-79.
10
Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.基于深度 Q 网络的车对车通信中的节能资源分配。
Sensors (Basel). 2023 Jan 23;23(3):1295. doi: 10.3390/s23031295.

引用本文的文献

1
Path loss modeling based on neural networks and ensemble method for future wireless networks.基于神经网络和集成方法的未来无线网络路径损耗建模
Heliyon. 2023 Sep 6;9(9):e19685. doi: 10.1016/j.heliyon.2023.e19685. eCollection 2023 Sep.
2
Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections.基于神经进化的自适应天线阵波束形成方案,提高交叉口 V2V 通信性能。
Sensors (Basel). 2021 Apr 23;21(9):2956. doi: 10.3390/s21092956.
3
Deterministic 3D Ray-Launching Millimeter Wave Channel Characterization for Vehicular Communications in Urban Environments.

本文引用的文献

1
Spatial Characterization of Radio Propagation Channel in Urban Vehicle-to-Infrastructure Environments to Support WSNs Deployment.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
Sensors (Basel). 2017 Jun 7;17(6):1313. doi: 10.3390/s17061313.
用于城市环境中车辆通信的确定性三维射线发射毫米波信道特性分析
Sensors (Basel). 2020 Sep 16;20(18):5284. doi: 10.3390/s20185284.
4
Path Loss Prediction based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network and Gaussian Process.基于机器学习技术的路径损耗预测:主成分分析、人工神经网络和高斯过程。
Sensors (Basel). 2020 Mar 30;20(7):1927. doi: 10.3390/s20071927.