Resource allocation optimization and performance evaluation for 5G cellular vehicle-to-everything (C-V2X)

doi:10.3969/j.issn.1674-8484.2023.03.010

Abstract

Abstract:

To improve the spectrum utilization efficiency of C-V2X (cellular vehicle to everything), a resource allocation algorithm was proposed to maximize system downlink throughput and ensure the connectivity of the V2V (vehicle to vehicle) communication. A channel model of cellular vehicle to everything was defined, the optimization model was established under the constraints of maximum transmission power, outage probability, etc., and was solved step by step. The KM (Kuhn-Munkras) algorithm was used to dynamically schedule channel resources to realize the dynamic allocation of network resources between the V2I (vehicle to infrastructure) downlink and the V2V link in the C-V2X communication system. The optimization performance of the algorithm was evaluated through different traffic scenarios. The results show that the proposed algorithm enters a steady state after seven iterations, achieves the optimal allocation of V2I link resources while ensuring the connectivity of V2V links, and the downlink spectrum efficiency increased by an average of more than 3.5% compared to the greedy algorithm.

Key words: intelligent transportation, vehicle-to-everything (V2X), cellular-V2X (C-V2X), resource allocation, throughput

CLC Number:

TN919.2

HONG Ying, SHA Yuchen, DING Fei, CHEN Zhu, ZHANG Dengyin. Resource allocation optimization and performance evaluation for 5G cellular vehicle-to-everything (C-V2X)[J]. Journal of Automotive Safety and Energy, 2023, 14(3): 346-354.

Figures/Tables 12

References 22

[1]	李克强. 我看智能网联汽车十年发展[J]. 智能网联汽车, 2022(3): 6-9.
	LI Keqiang. I see the ten-year development of intelligent connected vehicles[J]. Intell Connec Vehi, 2022(3): 6-9. (in Chinese)
[2]	Elfatih N M, Hasan M Kamrul, Kamal Z, et al. Internet of vehicle’s resource management in 5G networks using AI technologies: Current status and trends[J]. IET Commu, 2021, 16(5): 400-420.
[3]	郭静秋, 方守恩, 曲小波, 等. 基于强化协作博弈方法的双车道混合交通流特性[J]. 同济大学学报(自然科学版), 2019, 47(7): 976-983.
	GUO Jingqiu, FANG Shouen, QU Xiaobo, et al. Characteristics of two-lane mixed traffic flow based on enhanced cooperative game method[J]. J Tongji Univ (Nat Sci Ed), 2019, 47(7): 976-983. (in Chinese)
[4]	丁飞, 张楠, 李升波, 等. 智能网联车路云协同系统架构与关键技术研究综述[J]. 自动化学报, 2022, 48(12): 2863-2885.
	DING Fei, ZHANG Nan, LI Shengbo, et al. Overview of the research on the architecture and key technologies of the intelligent networked vehicle-road cloud collaboration system[J]. J Automation, 2022, 48(12): 2863-2885. (in Chinese)
[5]	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.
	ZHANG Haixia, LI Xianxian, LI Dongyang, et al. Research on key technologies of intelligent Internet of Vehicles based on vehicle behavior analysis[J]. J Elec Info, 2020, 42(1): 36-49. (in Chinese)
[6]	唐群, 朱国强. 基于MEC的蜂窝网络联合计算与无线资源管理[J]. 计算机工程与应用, 2020, 56(14): 82-87. doi: 10.3778/j.issn.1002-8331.1904-0386
	TANG Qun, ZHU Guoqiang. MEC based cellular network joint computing and wireless resource management[J]. Compu Engi Appl, 2020, 56(14): 82-87. (in Chinese)
[7]	吴杜成, 翟维维, 冯丛丛, 等. 5G异构蜂窝网络资源管理研究[J]. 移动通信, 2018, 42(3): 89-96.
	WU Ducheng, ZHAI Weiwei, FENG Congcong, et al. Research on resource management of 5G heterogeneous cellular network[J]. Mobi Commu, 2018, 42(3): 89-96. (in Chinese)
[8]	韩珍珍, 周末, 刘恩慧, 等. 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[J]. 电子与信息学报, 2021, 43(5): 1339-1348.
	HAN Zhenzhen, ZHOU Mo, LIU Enhui, et al. Resource allocation method of cellular vehicle networking and vehicular Ad-Hoc network heterogeneous vehicle networking based on user personalized service quality[J]. J Elec Info, 2021, 43(5): 1339-1348. (in Chinese)
[9]	WU Qiong, ZHAO Yu, FAN Qiang. Time-dependent performance modeling for platooning communications at intersection[J]. IEEE Internet Things J, 2022, 9(19): 18500-18513. doi: 10.1109/JIOT.2022.3161028 URL
[10]	LIANG Le, YE Hao, LI Geoffrey Ye. Spectrum sharing in vehicular networks based on multi-agent reinforcement learning[J]. IEEE J Selec Areas Commu, 2019, 37(10): 2282-2292.
[11]	Brahmi I, Hamdi M, Zarai F. Chaotic Grey Wolf Optimization-based resource allocation for Vehicle-to-Everything communications[J]. Int’l J CommuSyst, 2021, 34(13): 4908-4926.
[12]	Hussein H, Mohamed H R, Hussein A E, et al. Depth-first-search-tree based D2D power allocation algorithms for V2I/V2V shared 5G network resources[J]. Wireless Networks, 2021, 27(5): 3179-3193. doi: 10.1007/s11276-021-02649-4
[13]	李方伟, 张海波, 王子心. 车联网中基于MEC的V2X协同缓存和资源分配[J]. 通信学报, 2021, 42(2): 26-36. doi: 10.11959/j.issn.1000-436x.2021007
	LI Fangwei, ZHANG Haibo, WANG Zixin. MEC based V2X collaborative caching and resource allocation in the Internet of Vehicles[J]. J Communications, 2021, 42(2): 26-36. (in Chinese)
[14]	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)
[15]	LIU Gang, WANG Zhiqing, HU Jiewen, et al. Cooperative NOMA broadcasting/multicasting for low-latency and high-reliability 5G cellular V2X communications[J]. IEEE Internet Things J, 2019, 6(5): 7828-7838. doi: 10.1109/JIoT.6488907 URL
[16]	ZHU Hongbiao, WU Qiong, WU Xiaojun, et al. Decentralized power allocation for MIMO-NOMA vehicular edge computing based on deep reinforcement learning[J]. IEEE Internet Things J, 2022, 9(4): 12770-12782. doi: 10.1109/JIOT.2021.3138434 URL
[17]	3GPP TSG SA1. 3GPP TS 22.186: Service requirements for enhanced V2 X scenarios (V16.1.0)[S/OL]. (2017-03-09). https://www.3gpp.org/ftp/Specs/archive/22_series/22.186.
[18]	3GPP TSG SA 2. 3GPP TS 23.287: Architecture enhancements for 5G System (5GS) to support Vehicle-to-Everything (V2X)services (V16.5.0)[S/OL]. (2020-12-17). https://www.3gpp.org/ftp/Specs/archive/23_series/23.287.
[19]	3GPP TSG RAN 1. 3GPP TS 37.885: Study on evaluation methodology of new Vehicle-to-Everything V2X use cases for LTE and NR (V1.0.0)[S/OL]. (2018-05-11). https://www.3gpp.org/ftp/Specs/archive/37_series/37.885.
[20]	田兴鹏, 朱晓荣, 朱洪波. 基于KM算法的分布式无线节点任务分配方法[J]. 北京邮电大学学报, 2020, 43(6): 96-102. doi: 10.13190/j.jbupt.2020-089
	TIAN Xingpeng, ZHU Xiaorong, ZHU Hongbo. Distributed wireless node task allocation method based on KM algorithm[J]. J Beijing Univ Posts and Telecom, 2020, 43(6): 96-102. (in Chinese)
[21]	Rajeev R, Navneet A, Sunil J. Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques[J]. IET Commu, 2020, 14(9): 1502-1509.
[22]	郑银香, 侯帅, 沈旭, 等. 车联网典型应用场景业务建模及仿真方法[J]. 移动通信, 2020, 489(11): 80-85.
	ZHENG Yinxiang, HOU Shuai, SHEN Xu, et al. Traffic modeling and simulation method for typical application scenarios of Internet of vehicles[J]. Mobi Commu, 2020, 489(11): 80-85. (in Chinese)

载波频率，f_c	2 GHz
带宽，W	10 MHz
基站的高度， h_BS	35 m
车辆的高度， h_UT	1.5 m
车辆天线增益， G_antenna	3 dB
噪声功率， σ²	-114 dB
V2I链路最小容量， C₀	0.5 bit/s
V2V链路可容忍信干噪比， SINR₀	5 dB
V2I、V2V最大传输功率， P_max	23 dB
V2I链路总数，K	20
V2V链路总数，M	20
V2V链路可容忍的中断概率， p₀	0.001

载波频率，f_c	2 GHz
带宽，W	10 MHz
基站的高度， h_BS	35 m
车辆的高度， h_UT	1.5 m
车辆天线增益， G_antenna	3 dB
噪声功率， σ²	-114 dB
V2I链路最小容量， C₀	0.5 bit/s
V2V链路可容忍信干噪比， SINR₀	5 dB
V2I、V2V最大传输功率， P_max	23 dB
V2I链路总数，K	20
V2V链路总数，M	20
V2V链路可容忍的中断概率， p₀	0.001