基于用户个性化服务质量的蜂窝车联网与车载自组织网异构车联网资源分配方法

韩珍珍; 周末; 刘恩慧; 徐川; 赵国锋

doi:10.11999/JEIT200429

基于用户个性化服务质量的蜂窝车联网与车载自组织网异构车联网资源分配方法

doi: 10.11999/JEIT200429

重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 国家重点研发计划(2018YBF1800301, 2018YBF1800304)，重庆市研究生科研创新项目(CYB18175, BYJS201803)，重庆市技术创新与应用发展专项重大主题专项(cstc2019jscx-zdztzxX0013)

详细信息

作者简介:
韩珍珍：女，1989年生，博士生，研究方向为软件定义天地一体化网络组网路由、网络资源优化

周末：男，1991年生，硕士生，研究方向为无线网络资源优化

刘恩慧：女，1996年生，硕士生，研究方向为无线网络资源优化

徐川：男，1980年生，教授，研究方向为天地一体化网络、工业互联网、软件定义网络和网络测量

赵国锋：男，1974年生，教授，研究方向为天地一体化网络、工业物联网和网络测量

通讯作者:
赵国锋　zhaogf@cqupt.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 1049
- HTML全文浏览量: 290
- PDF下载量: 90
- 被引次数: 0
出版历程
- 收稿日期: 2020-05-29
- 修回日期: 2021-01-22
- 网络出版日期: 2021-02-03
- 刊出日期: 2021-05-18

A Personalized QoS-based Resource Allocation for Cellular-Vehicle to Everything Network and Vehicle Ad-hoc Network Heterogeneous Vehicular Network

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Key Research and Development Project (2018YBF1800301, 2018YBF1800304)，Chongqing Graduate Research and Innovation Project (CYB18175, BYJS201803)，The Major Theme Special Project of Chongqing Technology Innovation and Application Development Special Project (cstc2019jscx-zdztzxX0013)

摘要

摘要: 蜂窝车联网(C-V2X)与车载自组织网络(VANET)的异构融合能够有效提高网络容量。然而，不同网络在非授权频段上共存而引起的信道冲突会导致系统吞吐量降低和用户接入时延增大，无法满足车联网用户对服务质量(QoS)的需求。针对该问题，该文提出一种基于用户个性化QoS需求的时频资源分配方法。首先，分别对C-V2X 和 VANET 的吞吐量和时延进行建模分析，刻画用户数据传输时间配置与吞吐量和时延的数学关系；然后，基于上述模型构建吞吐量-时延联合优化函数，根据用户的个性化QoS需求实现异构网络中吞吐量和时延的优化；最后，提出一种基于改进多目标粒子群优化的时延-吞吐量联合优化算法(DT-JOA)进行求解。仿真结果表明，该文所提网络资源分配算法可以有效地保证用户的个性化QoS需求，提升异构网络综合性能。
- 蜂窝车联网 /
- 车载自组织网络 /
- 异构网络 /
- 资源分配
Abstract: The heterogeneous integration of Cellular-Vehicle to everything (C-V2X) and Vehicle Ad-hoc NETwork (VANET) can effectively increase network capacity. However, the channel conflicts caused by the coexistence of different networks on the unlicensed frequency bands will cause the system throughput to decrease and the user access delay to increase, which can not satisfy the Quality of Service (QoS) requirements. Considering this problem, a time-frequency resource allocation method based on personalized QoS is proposed. Firstly, the throughput and delay models of C-V2X and VANET are established respectively to determine the mathematical relationship between user data transmission time configuration and throughput and delay. Then, based on the above mathematical models, a Delay-Throughput Joint Optimization Algorithm (DT-JOA) is established to optimize throughput and delay in a heterogeneous network according to the personalized QoS requirements of users. Finally, a joint optimization algorithm for delay and throughput based on Particle Swarm Optimization (PSO) is proposed. The simulation results show that the proposed algorithm can meet the personalized QoS requirements of users and significantly improve the comprehensive performance of heterogeneous networks.
- Cellular-Vehicle to everything(C-V2X) /
- Vehicle Ad-hoc NETwork(VANET) /
- Heterogeneous network /
- Resources allocation

HTML全文

图 1 C-V2X与VANET异构车联网场景模型

下载: 全尺寸图片幻灯片

图 2 C-V2X与VANET异构车联网用户个性化QoS联合优化策略

下载: 全尺寸图片幻灯片

图 3 MO-PSO与其他算法的对比

下载: 全尺寸图片幻灯片

图 4 C-V2X对VANET用户的影响

下载: 全尺寸图片幻灯片

图 5 采用方案后C-V2X对VANET用户的影响

下载: 全尺寸图片幻灯片

图 6 紧急用户的时延分布

下载: 全尺寸图片幻灯片

图 7 非紧急用户的吞吐量下降情况

下载: 全尺寸图片幻灯片

表 1 基于PSO的联合优化算法

输入：最大迭代次数: maxgen, 粒子群数量: pop, 学习因子:C₁,C₂。
输出：Pareto最优解.
(1) 初始化:v, p, Pbest, Gbest, ω, t.
(2) while t < maxgen do
(3) for i = 1, 2, ···, pop do
(4) 计算适应度 F(x_i)
(5) if F(x_i(t)) < F(x_i(t+1)) then
(6) 更新局部Pbest= x_i(t+1);
(7) end if
(8) if F(Gbest(t)) < F(Gbest(t+1)) then
(9) 更新全局 Gbest (t+1) < Pbest (t+1)
(10) end if
(11) 针对每个粒子进行
(12) 速度比较 ${v_i}(t + 1)$;
(13) 位置比较 ${x_i}(t + 1)$;
(14) end for
(15) t++;
(16) end for
(17) end while

下载: 导出CSV

表 2 仿真参数

参数	数值
仿真区域	1000 m×1000 m
发射功率	43 dBm
功率增益因子	–31.5 dB
车辆数量	100
接收功率门限	–75 dBm
SINR门限	20 dB
EU容忍最大时延	50 ms
nEU最低数据速率	5 Mbps
子信道带宽	15 kHz
子信道数量	12
数据包大小	512 B
噪声功率	–114 dBm
车辆行驶速度	30～120 km/h
MAC层	IEEE 802.11 p
仿真时间	500 s
仿真次数	50

下载: 导出CSV

参考文献(16)

[1]	钱志鸿, 田春生, 郭银景, 等. 智能网联交通系统的关键技术与发展[J]. 电子与信息学报, 2020, 42(1): 2–19. doi: 10.11999/JEIT190787 QIAN Zhihong, TIAN Chunsheng, GUO Yinjing, et al. The key technology and development of intelligent and connected transportation system[J]. Journal of Electronics &Information Technology, 2020, 42(1): 2–19. doi: 10.11999/JEIT190787
[2]	张海霞, 李腆腆, 李东阳, 等. 基于车辆行为分析的智能车联网关键技术研究[J]. 电子与信息学报, 2020, 42(1): 36–49. doi: 10.11999/JEIT190820 ZHANG Haixia, LI Tiantian, LI Dongyang, et al. Research on vehicle behavior analysis based technologies for intelligent vehicular networks[J]. Journal of Electronics &Information Technology, 2020, 42(1): 36–49. doi: 10.11999/JEIT190820
[3]	CHEN Shanzhi, HU Jinling, YAN Shi, et al. Vehicle-to-everything (v2x) services supported by LTE-based systems and 5G[J]. IEEE Communications Standards Magazine, 2017, 1(2): 70–76. doi: 10.1109/MCOMSTD.2017.1700015
[4]	ABBAS F, FAN Pingzhi, KHAN Z, et al. A novel low-latency V2V resource allocation scheme based on cellular V2X communications[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(6): 2185–2197. doi: 10.1109/TITS.2018.2865173
[5]	QI Weijing, LANDFELDT B, SONG Qingyang, et al. Traffic differentiated clustering routing in DSRC and C-V2X hybrid vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2020, 69(7): 7723–7734. doi: 10.1109/TVT.2020.2990174
[6]	WANG Pengfei, DI Boya, ZHANG Hongliang, et al. Cellular V2X communications in unlicensed spectrum: Harmonious coexistence With VANET in 5G systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(8): 5212–5224. doi: 10.1109/TWC.2018.2839183
[7]	WEI Qing, LI Wang, FENG Zhiyong, et al. Wireless resource management in LTE-U driven heterogeneous V2X communication networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(8): 7508–7522. doi: 10.1109/TVT.2018.2823313
[8]	HUANG Xumin, YU Rong, KANG Jiawen, et al. Exploring mobile edge computing for 5G-enabled software defined vehicular networks[J]. IEEE Wireless Communications, 2017, 24(6): 55–63. doi: 10.1109/MWC.2017.1600387
[9]	DUO Ran, WU C, YOSHINAGA T, et al. SDN-based handover scheme in cellular/IEEE 802.11p Hybrid vehicular networks[J]. Sensors, 2020, 20(4): 1082. doi: 10.3390/s20041082
[10]	LI Xiaoshuai, MA Lin, SHANKARAN R, et al. Joint power control and resource allocation mode selection for safety-related V2X communication[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 7970–7986. doi: 10.1109/TVT.2019.2921352
[11]	AMADEO M, CAMPOLO C, and MOLINARO A. Enhancing IEEE 802.11p/WAVE to provide infotainment applications in VANETs[J]. Ad Hoc Networks, 2012, 10(2): 253–269. doi: 10.1016/j.adhoc.2010.09.013
[12]	CHEN Chen, WANG Baoji, and ZHANG Rongqing. Interference hypergraph-based resource allocation (IHG-RA) for NOMA-integrated V2X networks[J]. IEEE Internet of Things Journal, 2019, 6(1): 161–170. doi: 10.1109/JIOT.2018.2875670
[13]	DI Boya, SONG Lingyang, and LI Yonghui. Sub-channel assignment, power allocation, and user scheduling for non-orthogonal multiple access networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(11): 7686–7698. doi: 10.1109/TWC.2016.2606100
[14]	LEE K, KIM J, PARK Y, et al. Latency of cellular-based V2X: Perspectives on TTI-proportional latency and TTI-independent latency[J]. IEEE Access, 2017, 5: 15800–15809. doi: 10.1109/ACCESS.2017.2731777
[15]	TONG Weiyang, CHOWDHURY S, and MESSAC A. A multi-objective mixed-discrete particle swarm optimization with multi-domain diversity preservation[J]. Structural and Multidisciplinary Optimization, 2016, 53(3): 471–488. doi: 10.1007/s00158-015-1319-8
[16]	PRASAD S and RAMESH J. Partial transmit sequence based PAPR reduction with GA and PSO optimization techniques[C]. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2017: 1–4. doi: 10.1109/ICIIECS.2017.8276004.