高负载场景中考虑噪声及隐藏终端的C-V2X模式4性能分析

doi:10.3969/j.issn.1674-8484.2024.06.016

汽车安全与节能学报 ›› 2024, Vol. 15 ›› Issue (6): 943-951.DOI: 10.3969/j.issn.1674-8484.2024.06.016

高负载场景中考虑噪声及隐藏终端的C-V2X模式4性能分析

窦祖芳¹(), 王鹏¹(), 杨乔礼¹, 杨喜娟²

1.兰州交通大学自动化与电气工程学院，兰州 730070，中国
2.兰州交通大学电子与信息工程学院，兰州 730070，中国

收稿日期:2023-08-17 修回日期:2024-04-02 出版日期:2024-12-31 发布日期:2025-01-01
作者简介:窦祖芳（1984—），女（汉），甘肃，副教授。E-mail：douzufang@126.com。
王鹏（1997—），男(汉)，甘肃，硕士研究生。E-mail：473417339@qq.com。
基金资助:
国家自然科学基金资助项目(72171106);甘肃省自然科学基金资助项目(21JR7RA303);甘肃省教育厅高校创新基金资助项目(2022A-036);兰州交通大学青年科学基金资助项目(2021040)

C-V2X mode 4 performance analysis considering noise and hidden terminals in high-load scenarios

DOU Zufang¹(), WANG Peng¹(), YANG Qiaoli¹, YANG Xijuan²

1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070,China

Received:2023-08-17 Revised:2024-04-02 Online:2024-12-31 Published:2025-01-01

摘要/Abstract

摘要：

为深入探究基于蜂窝通信的车联网（C-V2X）模式4在高负载场景中的数据传输性能，提出了一个综合考虑随机噪声、随机阴影衰落及隐藏终端影响的数据传输分析模型。建立了一个融合噪声与阴影衰落的双重随机信噪比和信干噪比模型，并采用Cauchy分布对该模型进行求解；推导出数据传输过程中4种典型错误概率的解析式，揭示噪声、阴影衰落与数据冲突之间的耦合关系；基于4种错误类型，进一步构建集噪声、阴影衰落和隐藏终端影响于一体的数据包传输成功率（PDR）解析模型; 最后，利用数值仿真深入分析影响通信效率的关键参数，并探讨各个参数对PDR的影响规律。结果表明：在高噪声、高移速、高负载环境下，PDR较前期文献降低了16.7%，验证了随机噪声和隐藏终端对通信造成的不良影响。

关键词: 蜂窝通信的车联网（C-V2X）模式, 双随机信噪比模型, Cauchy分布, 数据包传输成功率（PDR）

Abstract:

A data transmission analysis model, which took into account random noise, random shadow fading and hidden terminal, was proposed to further explore the data transmission performance of Cellular Vehicle-to-Everything (C-V2X) mode 4 in high-load scenarios. Firstly, a double random signal-to-noise ratio and signal-to-interference-to-noise ratio model was established, and the Cauchy distribution was used to solve the model. Secondly, the analytical formulas of four typical error probabilities in the process of data transmission were derived, and the coupling relationship between noise, shadow fading and data conflict was revealed. Thirdly, based on the four error types, the packet delivery ratio analytical model integrating noise, shadow fading and hidden terminal influence was further constructed. Finally, numerical simulations were used to analyze the key parameters affecting communication efficiency, and the influence of each parameter on packet delivery ratio was discussed. The results show that the packet delivery ratio (PDR) is reduced by 16.7% compared with the previous literature in the environment of high noise, high moving speed and high load, which verifies the adverse effects of random noise and hidden terminals on communication.

Key words: cellular vehicle-to-everything (C-V2X) mode, double random signal-to-noise model, Cauchy distribution, packet delivery ratio (PDR)

中图分类号:

TN929.2

窦祖芳, 王鹏, 杨乔礼, 杨喜娟. 高负载场景中考虑噪声及隐藏终端的C-V2X模式4性能分析[J]. 汽车安全与节能学报, 2024, 15(6): 943-951.

DOU Zufang, WANG Peng, YANG Qiaoli, YANG Xijuan. C-V2X mode 4 performance analysis considering noise and hidden terminals in high-load scenarios[J]. Journal of Automotive Safety and Energy, 2024, 15(6): 943-951.

图/表 9

图1 C-V2X通信网络架构

图2 S-SPS机制资源的预留和选择过程

图3 S-SPS机制资源选择流程图

图4 两车辆之间候选资源重叠示意图

图5 V2V通信场景

发射功率	20、23 dBm
车辆密度	0.1、0.2、0.3 辆 / m
信道带宽	10 MHz
数据传输率	10、20 Hz
单子帧子信道数	2、4
数据大小	190 bytes
感知功率阈值	-90.3 dBm

表1 参数设置

图6 接收错误的概率和传播错误的概率对比

图7 不同车辆密度下对应的分组冲突率对比

图8 不同参数下的传输成功率对比

参考文献 21

[1]	周兴凯, 窦祖芳, 杨喜娟, 等. 基于IEEE 802.11p的自适应主次窗口退避机制及验证[J]. 汽车工程, 2022, 44(12): 1856-1865.
	ZHOU Xingkai, DOU Zufang, YANG Xijuan, et al. Adaptive primary and secondary window retreat mechanism and verification based on IEEE 802.11p[J]. Autom Engi, 2022, 44(12): 1856-1865. (in Chinese)
[2]	崔佩佩. 基于C-V2X模式4车载通信性能的研究[D]. 大连理工大学, 2020.
	CUI Peipei. Research on vehicle communication performance based on C-V2X mode4[D]. Dalian: University of Technology, 2020. (in Chinese)
[3]	3GPP TSG SA1 3GPP TS 22.186: Service requirements for enhanced V2X scenarios (V16.1.0)[S/OL]. (2017-03-09) https://www.3gpp.org/ftp/Specs/archive/22_series/22.186.
[4]	王燕燕, 齐丽娜. 基于D2D的车联网中无线资源分配研究[J]. 南京邮电大学学报(自然科学版), 2018, 38(4): 55-60.
	WANG Yanyan, QI Lina. Radio resource allocation for D2D-based vehicle network[J]. J Nanjing Univ Post Telecom (Nat Sci), 2018, 38(4): 55-60. (in Chinese)
[5]	Gonzalez-Martin M, Sepulcre M, Molina-Masegosa R, et al. Analytical models of the performance of C-V2X Mode 4 vehicular communications[J]. IEEE Trans Vehi Tech, 2019, 68(2): 1155-1166.
[6]	Toghi B, Saifuddin M, Mahjoub H N, et al. Multiple access in cellular V2X: Performance analysis in highly congested vehicular networks [C]// 2018 IEEE Vehi Net Conf (VNC), Taipai, China, 2018: 1-8.
[7]	Nabil A, Marojevic V, Kaur K, et al. Performance analysis of sensing-based semi-persistent scheduling in C-V2X networks [C]// IEEE 88th Vehi Tech Conf (VTC), Chicago, IL, USA, 2018: 1-5.
[8]	Eckermann F, Kahlert M, Wietfeld C. Performance analysis of C-V2X Mode 4 communication introducing an open-source C-V2X simulator[C]// IEEE 90th Vehi Tech Conf, Honolulu, HI, USA, 2019: 1-5.
[9]	LI Wenfeng, MA Xiaomin, WU Jun, et al. Analytical model and performance evaluation of long-term evolution for vehicle safety services[J]. IEEE Trans Vehi Tech, 2017, 66(3): 1926-1939.
[10]	Wijesiri G P, Haapola J, Samarasinghe T. The effect of multiple access categories on the MAC layer performance of IEEE 802.11p[C]// IEEE Glo Commu Conf, Taipai, China, 2020: 1-6.
[11]	Wijesiri G P, Haapola J, Samarasinghe T. A discrete time Markov chain based comparison of the MAC layer performance of C-V2X mode 4 and IEEE 802.11p[J]. IEEE Trans Commun, 2021, 69(4): 2505-2517.
[12]	HE Xinxin, Lyu Jie, ZHAO Jiaqi, et al. Design and analysis of a short-term sensing based resource selection scheme for C-V2X networks[J]. IEEE Internet Things J, 2020, 7(11): 11209-11222.
[13]	YANG Jiaxin J, Pelletier B, Champagne B. Enhanced autonomous resource selection for LTE-based V2V communication [C]// 2016 IEEE Vehi Net Conf (VNC), Columbus, OH, USA, 2016. 1-6
[14]	Jung S Y, Cheon H R, Kim J H. Reducing consecutive collisions in sensing based semi persistent scheduling for cellular-V2X [C]// IEEE 90th Vehi Tech Conf (VTC), Columbus, OH, USA, 2019: 1-5.
[15]	Ha S, Yoo W, Kim H, et al. C-V2X Adaptive short-term sensing scheme for enhanced DENM and CAM communication[J]. IEEE Wireless Commu Letts, 2022, 11(3): 593-597.
[16]	余翔, 陈晓东, 王政, 等. 基于LTE-V2X的车联网资源分配算法[J]. 计算机工程, 2021, 47(2): 188-193. doi: 10.19678/j.issn.1000-3428.0056935
	YU Xiang, CHENG Xiaodong, WANG Zheng, et al. Resource allocation algorithm of Internet of vehicles based on LTE-V2X[J]. Comput Engi, 2021, 47(2): 188-193. (in Chinese)
[17]	YANG Yan, DANG Shuping, HE Yejun, et al. Markov decision-based pilot optimization for 5G V2X vehicular communications[J]. IEEE Internet Things J, 2019, 6(1): 1090-1103.
[18]	廖勇, 花远肖, 姚海梅, 等. 高速移动环境下基于深度学习的信道估计方法[J]. 电子学报, 2019, 47(8): 1701-1707. doi: 10.3969/j.issn.0372-2112.2019.08.013
	LIAO Yong, HUA Yuanxiao, YAO Haimei, et al. Channel estimation method based on deep learning in high-speed mobile environments[J]. Acta Elect, 2019, 47(8): 1701-1707. (in Chinese)
[19]	WU Qiong, SHI Shuai, WAN Ziyang, et al. Towards V2I age-aware fairness access: A DQN based intelligent vehicular node training and test method[J]. Chin J Elect, 2023, 32(6): 1230-1244.
[20]	WU Qiong, ZHAO Yu, FAN Qiang, et al. Mobility-aware cooperative caching in vehicular edge computing based on asynchronous federated and deep reinforcement learning[J]. IEEE J Select Top Sig Proc, 2023, 17(1): 66-81.
[21]	Bazzi A, Masini B M, Zanella A, et al. On the Performance of IEEE 802.11p and LTE-V2V for the cooperative awareness of connected vehicles[J]. IEEE Trans Vehi Tech, 2017, 66(11): 10419-10432.

高负载场景中考虑噪声及隐藏终端的C-V2X模式4性能分析

C-V2X mode 4 performance analysis considering noise and hidden terminals in high-load scenarios

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 21

相关文章 0

编辑推荐

Metrics

本文评价

期刊信息

在线期刊

作者中心

审稿中心

联系我们