物联网学报 ›› 2020, Vol. 4 ›› Issue (3): 96-105.doi: 10.11959/j.issn.2096-3750.2020.00178

• 专题:智慧交通物联网 • 上一篇    下一篇

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

郭彩丽1,2(),陈九九1,宣一荻1,张荷1   

  1. 1 北京邮电大学,北京 100876
    2 先进信息网络北京实验室,北京 100876
  • 修回日期:2020-07-16 出版日期:2020-09-30 发布日期:2020-09-07
  • 作者简介:郭彩丽(1977- ),女,山西长治人,博士,北京邮电大学教授,主要研究方向为认知无线电、车联网、视觉智能计算等|陈九九(1994- ),男,湖南岳阳人,北京邮电大学博士生,主要研究方向为车联网、通信与计算资源联合优化等|宣一荻(1995- ),女,天津人,北京邮电大学硕士生,主要研究方向为认知无线电、频谱共享等|张荷(1995- ),女,山东淄博人,北京邮电大学硕士生,主要研究方向为认知无线电、频谱感知等
  • 基金资助:
    北京市自然科学基金资助项目(4202049);国家重点研发计划(2018YFB1800805)

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

Caili GUO1,2(),Jiujiu CHEN1,Yidi XUAN1,He ZHANG1   

  1. 1 Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 Beijing Laboratory of Advanced Information Networks,Beijing 100876,China
  • Revised:2020-07-16 Online:2020-09-30 Published:2020-09-07
  • Supported by:
    The Beijing Natural Science Foundation(4202049);The National Key R&D Program of China(2018YFB1800805)

摘要:

随着全球汽车产业智能化和网联化的爆发式发展,作为车联网重要支撑的通信技术面临着频谱资源紧缺的难题。除了提供安全服务以外,车联网服务需求的多样性使得引入认知无线电技术成为有效的解决途径,可以实现与授权用户共享sub-6 GHz和毫米波的异质频谱资源,但车联网复杂动态变化环境的影响使得频谱利用率性能的提升受限。提出了充分利用潜在的多源动态时空数据挖掘和学习车辆轨迹、交通流的变化规律的方法,并利用其规律指导频谱的感知和共享。通过搭建系统级仿真平台进行仿真分析,结果表明所提方案的频谱效率得到有效提升。

关键词: 车联网, 动态时空数据, 异质频谱共享, 多服务的服务质量要求

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

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