• 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

多车车对车场景下 5.9GHz 信道非平稳性分析。

Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios.

机构信息

School of Automation, Wuhan University of Technology, Wuhan 430070, China.

Guangdong Communications and Networks Institute, Guangzhou 510700, China.

出版信息

Sensors (Basel). 2021 May 23;21(11):3626. doi: 10.3390/s21113626.

DOI:10.3390/s21113626
PMID:34070976
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8197023/
Abstract

The vehicle-to-vehicle (V2V) radio channel is non-stationary due to the rapid movement of vehicles. However, the stationarity of the V2V channels is an important indicator of the V2V channel characteristics. Therefore, we analyzed the non-stationarity of V2V radio channels using the local region of stationarity (LRS). We selected seven scenarios, including three directions of travel, i.e., in the same, vertical, and opposite directions, and different speeds and environments in a similar driving direction. The power delay profile (PDP) and LRS were estimated from the measured channel impulse responses. The results show that the most important influences on the stationary times are the direction and the speed of the vehicles. The average stationary times for driving in the same direction range from 0.3207 to 1.9419 s, the average stationary times for driving in the vertical direction are 0.0359-0.1348 s, and those for driving in the opposite direction are 0.0041-0.0103 s. These results are meaningful for the analysis of the statistical characteristics of the V2V channel, such as the delay spread and Doppler spread. Small-scale fading based on the stationary times affects the quality of signals transmitted in the V2V channel, including the information transmission rate and the information error code rate.

摘要

车对车(V2V)无线电通道由于车辆的快速移动而具有非平稳性。然而,V2V 信道的平稳性是 V2V 信道特性的一个重要指标。因此,我们使用局部平稳区域(LRS)来分析 V2V 无线电信道的非平稳性。我们选择了七个场景,包括三个行驶方向,即相同、垂直和相反方向,以及类似行驶方向的不同速度和环境。从测量的信道冲激响应中估计了功率延迟分布(PDP)和 LRS。结果表明,对平稳时间影响最大的是车辆的方向和速度。在同一方向行驶的平均平稳时间范围为 0.3207 到 1.9419 秒,在垂直方向行驶的平均平稳时间为 0.0359 到 0.1348 秒,在相反方向行驶的平均平稳时间为 0.0041 到 0.0103 秒。这些结果对于分析 V2V 信道的统计特性,如时延扩展和多普勒扩展,具有重要意义。基于平稳时间的小尺度衰落会影响 V2V 信道中传输信号的质量,包括信息传输速率和信息误码率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/d094446579af/sensors-21-03626-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/8993e6fa0ff2/sensors-21-03626-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/b89ab9f664a5/sensors-21-03626-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/8f29b8915bf9/sensors-21-03626-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/73d66eb57891/sensors-21-03626-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/96869c4aaeca/sensors-21-03626-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/47b684a3ccce/sensors-21-03626-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/c1ca7da1d222/sensors-21-03626-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/4f9eb84122fa/sensors-21-03626-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/a1fe79913b1d/sensors-21-03626-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/e7e306677e11/sensors-21-03626-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/6576034a1bea/sensors-21-03626-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/ed73c502840c/sensors-21-03626-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/51cd4c3c2a8e/sensors-21-03626-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/d7d871baa84f/sensors-21-03626-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/9c9adf060cd5/sensors-21-03626-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/d094446579af/sensors-21-03626-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/8993e6fa0ff2/sensors-21-03626-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/b89ab9f664a5/sensors-21-03626-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/8f29b8915bf9/sensors-21-03626-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/73d66eb57891/sensors-21-03626-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/96869c4aaeca/sensors-21-03626-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/47b684a3ccce/sensors-21-03626-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/c1ca7da1d222/sensors-21-03626-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/4f9eb84122fa/sensors-21-03626-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/a1fe79913b1d/sensors-21-03626-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/e7e306677e11/sensors-21-03626-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/6576034a1bea/sensors-21-03626-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/ed73c502840c/sensors-21-03626-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/51cd4c3c2a8e/sensors-21-03626-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/d7d871baa84f/sensors-21-03626-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/9c9adf060cd5/sensors-21-03626-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af7/8197023/d094446579af/sensors-21-03626-g016.jpg

相似文献

1
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.
2
Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments.不同路边散射环境下的非平稳时变车载信道特性。
Sci Rep. 2022 Aug 22;12(1):14344. doi: 10.1038/s41598-022-18592-z.
3
On the Stationarity Time of a Vehicle-to-Infrastructure Massive Radio Channel in a Line-of-Sight Suburban Environment.视距郊区环境下车路通信大规模无线信道的平稳时间
Sensors (Basel). 2022 Nov 2;22(21):8420. doi: 10.3390/s22218420.
4
A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations.一种适用于允许三维速度变化的非平稳车对车信道的新型几何双尺度模型。
Sensors (Basel). 2021 May 10;21(9):3271. doi: 10.3390/s21093271.
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
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.
7
LoRa-Based Physical Layer Key Generation for Secure V2V/V2I Communications.用于安全车对车/车对基础设施通信的基于LoRa的物理层密钥生成
Sensors (Basel). 2020 Jan 26;20(3):682. doi: 10.3390/s20030682.
8
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.
9
Crowdsourcing-Assisted Radio Environment Database for V2V Communication.用于车对车通信的众包辅助无线电环境数据库
Sensors (Basel). 2018 Apr 12;18(4):1183. doi: 10.3390/s18041183.
10
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.

本文引用的文献

1
Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing.面向移动众包感知的服务收益感知多任务分配策略。
Sensors (Basel). 2019 Oct 27;19(21):4666. doi: 10.3390/s19214666.
2
Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant.受住户活动影响的室内无线电信道的时频特性。
Sensors (Basel). 2019 Aug 15;19(16):3557. doi: 10.3390/s19163557.