Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Wang, X; Wang, Y; Cui, Q; Chen, KC; Ni, W

Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Wang, X Wang, Y Cui, Q Chen, KC Ni, W

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Access, 2022, 10, pp. 44710-44723
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Wang, Y
dc.contributor.author	Cui, Q
dc.contributor.author	Chen, KC
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.date.accessioned	2023-04-11T02:07:11Z
dc.date.available	2023-04-11T02:07:11Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Access, 2022, 10, pp. 44710-44723
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/169500
dc.description.abstract	Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the 'generalized closed-loop', where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2022.3168986
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-11T02:07:10Z
pubs.publication-status	Published
pubs.volume	10

Abstract:

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the 'generalized closed-loop', where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

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http://hdl.handle.net/10453/169500