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	A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks
	Pressas, Andreas; Sheng, Zhengguo; Ali, Falah; Tian, Daxin
刊名	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
	2019
卷号	68页码:9136-9150
关键词	Vehicular ad hoc networks machine learning access control fairness IEEE 802.11p link layer CSMA
ISSN号	0018-9545
DOI	10.1109/TVT.2019.2929035
URL标识	查看原文
收录类别	SCIE
WOS记录号	WOS:000487191500070
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/5916408
专题	北京航空航天大学
推荐引用方式 GB/T 7714	Pressas, Andreas,Sheng, Zhengguo,Ali, Falah,et al. A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2019,68:9136-9150.
APA	Pressas, Andreas,Sheng, Zhengguo,Ali, Falah,&Tian, Daxin.(2019).A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,68,9136-9150.
MLA	Pressas, Andreas,et al."A Q-Learning Approach With Collective Contention Estimation for Bandwidth-Efficient and Fair Access Control in IEEE 802.11p Vehicular Networks".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 68(2019):9136-9150.

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