车联网频谱捷变机制研究
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摘要
随着自动驾驶及道路安全通告等应用的发展与普及,车联网频谱资源变得越来越稀缺,而用频需求以及车联网频谱资源本身的动态性导致了现有频谱固定使用模式的低效.本文对车联网频谱捷变机制进行研究,提出了一个车联网频谱捷变架构,讨论了架构里所包含的车辆分组协议与频谱智能决策协议,同时,使用EXata仿真软件对所提频谱机制进行了评估.仿真结果证明了本文所提的车联网频谱捷变机制是有效的,可以根据实时频谱态势进行智能决策,及时地对频谱资源进行调整,从而解决频谱拥塞和干扰等问题,极大地提高频谱利用效率.
With the development and popularization of applications such as autonomous driving and road safety announcements,the spectrum resources of the Vehicular Ad Hoc Networks( VANETs) have become increasingly scarce,and the frequency demand and the dynamic nature of the spectrum resources of VANETs have led to the inefficiency of the existing fixed spectrum usage model.This paper studies the spectrum agility mechanism for VANETs,proposes a vehicular networks' spectrum agile architecture,discusses the vehicle grouping protocol and spectrum intelligent decision-making protocol contained in the architecture,and uses the EXata simulation software to implement the proposed spectrum architecture.The simulation results prove that the proposed spectrum agility mechanism for VANETs is effective,and can make intelligent decision based on real-time spectrum situation,adjust spectrum resources in time,solve problems such as spectrum congestion and interference,and greatly improve the efficiency of spectrum utilization.
With the development and popularization of applications such as autonomous driving and road safety announcements,the spectrum resources of the Vehicular Ad Hoc Networks( VANETs) have become increasingly scarce,and the frequency demand and the dynamic nature of the spectrum resources of VANETs have led to the inefficiency of the existing fixed spectrum usage model.This paper studies the spectrum agility mechanism for VANETs,proposes a vehicular networks' spectrum agile architecture,discusses the vehicle grouping protocol and spectrum intelligent decision-making protocol contained in the architecture,and uses the EXata simulation software to implement the proposed spectrum architecture.The simulation results prove that the proposed spectrum agility mechanism for VANETs is effective,and can make intelligent decision based on real-time spectrum situation,adjust spectrum resources in time,solve problems such as spectrum congestion and interference,and greatly improve the efficiency of spectrum utilization.
引文
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[13]ZHAO Y J,ZHENG Z,ZHANG X Y,et al.Q learning algorithm based UAV path learning and obstacle avoidence approach[C]//36th Chinese Control Conference,2017:3397-3402.
[14]SOHAN T A,HAQUE H H,HASAN M A,et al.Investigating the challenges of dynamic spectrum access in cognitive radio-enabled vehicular Ad Hoc networks(CR-VANETs)[C]//International Conference on Electrical Engineering and Information Communication Technology,2015:1-6.
[15]陈昕.认知无线电中基于机器学习的频谱感知技术研究[D].北京:北京邮电大学,2017.CHEN Xin.Research on machine learning based spectrum sensing in cognitive radio[D].Beijing:Beijing University of Post and Telecommunications,2017.(in Chinese)
[2]EZE J,ZHANG S,LIU E,et al.Cognitive radio technology assisted vehicular ad-hoc networks(VANETs):Current status,challenges,and research trends[C]//23rd International Conference on Automation and Computing,2017:1-6.
[3]祝思婷.车辆密集场景下的认知车联网频谱感知研究[D].北京:北京邮电大学,2017.ZHU Siting.Research on spectrum sensing in cognitive vehicular networks with dense traffic[D].Beijing:Beijing U-niversity of Post and Telecommunications,2017.(in Chinese)
[4]SHAH N,HABIBI D,AHMAD I.Multichannel cognitive medium access control protocol for vehicular Ad-Hoc networks[C]//IEEE Vehicular Technology Conference,2012:1-5.
[5]KASSLER A,KIM W,GERLA M,et al.Co Route:a new cognitive anypath vehicular routing protocol[C]//7th International Wireless Communications and Mobile Computing Conference,2011:766-771.
[6]CHUNGS,YOO J,KIM C.A cognitive MAC for VANETbased on the WAVE systems[C]//11th International Conference on Advanced Communication Technology,2009:41-46.
[7]AJALTOUNI H E,PAZZI R W,BOUKERCHE A.An efficient Qo S MAC for IEEE 802.11p over cognitive multichannel vehicular networks[C]//IEEE International Conference on Communications,2012:413-417.
[8]KHOSHKHOLGH M G,YAMCHI N M,NAVAIE K,et al.Radio resource allocation for OFDM-based dynamic spectrum sharing:duality gap and time averaging[J].IEEEJournal on Selected Areas in Communications,2015,33(5):848-864.
[9]唐迪.车联网MAC层关键技术研究[D].北京:北京理工大学,2015.TANG Di.Research on key technologies of MAC layer in VANET[D].Beijing:Beijing Institute of Technology,2015.(in Chinese)
[10]SAKHAEE E,JAMALIPOUR A.Stable clustering and communications in pseudolinear highly mobile Ad Hoc networks[J].IEEE Transactions on Vehicular Technology,2008,57(6):3769-3777.
[11]XU Z,WANG Y,TANG J.et al.A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs[C]//IEEE International Conference on Communications,2017:1-6.
[12]YE H,LI G Y.Deep reinforcement learning for resource allocation in V2V communications[C]//IEEE International Conference on Communications,2018:1-6.
[13]ZHAO Y J,ZHENG Z,ZHANG X Y,et al.Q learning algorithm based UAV path learning and obstacle avoidence approach[C]//36th Chinese Control Conference,2017:3397-3402.
[14]SOHAN T A,HAQUE H H,HASAN M A,et al.Investigating the challenges of dynamic spectrum access in cognitive radio-enabled vehicular Ad Hoc networks(CR-VANETs)[C]//International Conference on Electrical Engineering and Information Communication Technology,2015:1-6.
[15]陈昕.认知无线电中基于机器学习的频谱感知技术研究[D].北京:北京邮电大学,2017.CHEN Xin.Research on machine learning based spectrum sensing in cognitive radio[D].Beijing:Beijing University of Post and Telecommunications,2017.(in Chinese)
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