动态时空数据驱动的认知车联网频谱感知与共享技术研究

doi:10.11959/j.issn.2096-3750.2020.00178

Abstract

Abstract:

With the explosive development of the intellectualization and network connection of the global automobile industry,the communication technology as a crucial support of the Internet of vehicles (IoV) is facing the problem of spectrum shortage.In addition to providing security services,the diverse service demands of the IoV make the introduction of the cognitive radio technology an effective solution,which can share heterogeneous spectrum resources integrating the sub-6 GHz and millimeter-wave spectrum resources with primary users.But the performance is limited due to the influence of the complex dynamic environment of the IoV.To address this issue,a novelty method was proposed which aimed to make full use of the potential multi-source dynamic spatiotemporal data,mine and learn the changing rules of the vehicle trajectory and traffic flow,and the rules were used to guide the sensing and sharing of the spectrum resource.The system-level simulation platform was built for simulation analysis,the results showed that the performance of the proposed scheme was effectively improved.

Key words: Internet of vehicles, dynamic spatiotemporal data, heterogeneous spectrum sharing, multi-service quality of service requirement

CLC Number:

TN92

Caili GUO,Jiujiu CHEN,Yidi XUAN,He ZHANG. Research on the spectrum sensing and sharing technology in the dynamic spatiotemporal data driven cognitive Internet of vehicles[J]. Chinese Journal on Internet of Things, 2020, 4(3): 96-105.

Figures/Tables 6

References 25

[20]	XUAN Y D , GUO C L , FENG C Y ,et al. Multi-graph based spectrum sharing scheme in vehicular network with integration of heterogenous spectrum[C]// 2019 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2019: 1-6.
[21]	宣一荻 . 认知车联网中的异质频谱共享技术研究[D]. 北京：北京邮电大学, 2020.
	XUAN Y D . Research on heterogeneous spectrum sharing technologyin cognitive vehicular networks[D]. Beijing:Beijing University of Posts and Telecommunications, 2020.
[22]	LIANG L , LI G , XU W . Resource allocation for D2D-enabled vehicular communications[J]. IEEE Transactions on Communications, 2017,65(7): 3186-3197.
[23]	SUN P , ALJERI N , BOUKERCHE A . Machine learning-based models for real-time traffic flow prediction in vehicular networks[J]. IEEE Network, 2020,34(3): 178-185.
[24]	DUAN Z T , YANG Y , ZHANG K ,et al. Improved deep hybrid networks for urban traffic flow prediction using trajectory data[J]. IEEE Access, 2018,6: 31820-31827.
[1]	中国汽车工程学会.合作式智能运输系统车用通信系统应用层及应用数据交互标准[S]. 2018.
	China Society of Automotive Engineering. Cooperative intelligent transportation system,vehicular communication,application layer specification and data exchange standard[S]. 2018.
[2]	TIAN D X , ZHOU J S , WANG Y P ,et al. Channel access optimization with adaptive congestion pricing for cognitive vehicular networks:an evolutionary game approach[J]. IEEE Transactions on Mobile Computing, 2020,19(4): 803-820.
[3]	SALEEM M A , ZHOU S J , SHARIF A ,et al. Expansion of cluster head stability using fuzzy in cognitive radio CR-VANET[J]. IEEE Access, 2019,7: 173185-173195.
[4]	QIAN Y F , CHEN M , CHEN J ,et al. Secure enforcement in cognitive Internet of vehicles[J]. IEEE Internet of Things Journal, 2018,5(2): 1242-1250.
[5]	EZE J , ZHANG S J , LIU E J ,et al. Cognitive radio-enabled Internet of vehicles:a cooperative spectrum sensing and allocation for vehicular communication[J]. IET Networks, 2018,7(4): 190-199.
[6]	CHOI J , VA V , GONZALEZ-PRELCIC N ,et al. Millimeter-wave vehicular communication to support massive automotive sensing[J]. IEEE Communications Magazine, 2016,54(12): 160-167.
[7]	KONG L H , KHAN M K , WU F ,et al. Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles:overview,design,and challenges[J]. IEEE Communications Magazine, 2017,55(1): 62-68.
[8]	WANG Y F , ZHANG Z Y , LI H S . Universal quickest sensing of spectrum change in millimeter wave communications:a data driven approach[C]// 2017 IEEE Global Communications Conference (GLOBECOM). IEEE, 2017: 1-6.
[9]	LI L , HE X D , LI H S . Learning the spectrum using collaborative filtering in directional millimeter wave networks[C]// 2017 IEEE Global Communications Conference (GLOBECOM). IEEE, 2017: 1-7.
[10]	LU L , ZHOU X W , LI G Y . Optimal sequential detection in cognitive radio networks[C]// 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012.
[11]	ZHOU H B , CHENG N , YU Q ,et al. Toward multi-radio vehicular data piping for dynamic DSRC/TVWS spectrum sharing[J]. IEEE Journal on Selected Areas in Communications, 2016,34(10):25752588.
[12]	WU C , YOSHINAGA T , JI Y S . Cooperative content delivery in vehicular networks with integration of sub-6 GHz and mmWave[C]// 2017 IEEE Globecom Workshops (GC Wkshps). IEEE, 2017: 1-6.
[13]	DENG J Q , TIRKKONEN O , FREIJ-HOLLANTI R ,et al. Resource allocation and interference management for opportunistic relaying in integrated mmWave/sub-6 GHz 5G networks[J]. IEEE Communications Magazine, 2017,55(6): 94-101.
[14]	LIM J H , NAITO K , YUN J H ,et al. Reliable safety message dissemination in NLOS intersections using TV white spectrum[J]. IEEE Transactions on Mobile Computing, 2018,17(1): 169-182.
[15]	ZHOU H B , CHENG N , LU N ,et al. WhiteFi infostation:engineering vehicular media streaming with geolocation database[J]. IEEE Journal on Selected Areas in Communications, 2016,34(8): 2260-2274.
[16]	XU Z N , GAN M M , ZEMEN T . Cluster-based non-stationary vehicular channel model[C]// 2016 10th European Conference on Antennas and Propagation (EuCAP). IEEE, 2016: 1-6.
[17]	WANG Y F , ZHANG Z Y , LI H S . Universal quickest sensing of spectrum change in millimeter wave communications:a data driven approach[C]// 2017 IEEE Global Communications Conference (GLOBECOM). IEEE, 2017: 1-6.
[18]	MICHELUSI N , NOKLEBY M , MITRA U ,et al. Multi-scale spectrum sensing in small-cell mm-wave cognitive wireless networks[C]// 2017 IEEE International Conference on Communications (ICC). IEEE, 2017: 1-6.
[19]	ZHANG H , GUO C L . Beam alignment-based mmwave spectrum sensing in cognitive vehicular networks[C]// GlobalSIP 2019. IEEE, 2019: 1-6.
[25]	KHAN A , SUN L , IFEACHOR E . QoE prediction model and its application in video quality adaptation over UMTS networks[J]. IEEE Transactions on Multimedia, 2012,14(2): 431-442.

Metrics

Recommended 0

No Suggested Reading articles found!

	可靠性（SINR>5 dB）	分组丢失率（时延>传输时延门限）
CAITS	99.85%	0.4%（时延>10 ms）
ADITS	96.76%	3.2%（时延>100 ms）

Research on the spectrum sensing and sharing technology in the dynamic spatiotemporal data driven cognitive Internet of vehicles

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 25

Related Articles 7

Metrics

Recommended 0

[1]	Kai JIANG, Yue CAO, Huan ZHOU, Xuefeng REN, Yongdong ZHU, Hai LIN. Edge intelligence empowered internet of vehicles: concept, framework, issues, implementation, and prospect [J]. Chinese Journal on Internet of Things, 2023, 7(1): 37-48.
[2]	Yu YAO, Yanjie LI, Lenan WU, Pu MIAO, Xiaoyu TANG. Cognitive waveform design for radar-communication transceiver networks [J]. Chinese Journal on Internet of Things, 2021, 5(4): 71-80.
[3]	Xinxin FENG, Danying GUO, Zefeng LIU, Haifeng ZHENG. Task allocation in IoV-based crowdsensing combing clustering and CMAB [J]. Chinese Journal on Internet of Things, 2021, 5(3): 86-96.
[4]	Xuemin(Sherman) SHEN,Nan CHENG,Haibo ZHOU,Feng LYU,Wei QUAN,Weisen SHI,Huaqing WU,Conghao ZHOU. Space-air-ground integrated networks:review and prospect [J]. Chinese Journal on Internet of Things, 2020, 4(3): 3-19.
[5]	Qicheng ZENG,Yuxuan SUN,Sheng ZHOU. Computation task offloading algorithm and system level simulation for vehicles [J]. Chinese Journal on Internet of Things, 2019, 3(3): 62-69.
[6]	Kai XIONG,Supeng LENG,Ke ZHANG,Hao LIU. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing [J]. Chinese Journal on Internet of Things, 2019, 3(2): 20-27.
[7]	Guanhua QIAO,Supeng LENG,Hao LIU,Kaisheng HUANG,Fan WU. Task collaborative offloading scheme in vehicle multi-access edge computing network [J]. Chinese Journal on Internet of Things, 2019, 3(1): 51-59.