IRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles

Hieu, NQ; Hoang, DT; Luong, NC; Niyato, D

IRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles

Hieu, NQ Hoang, DT Luong, NC Niyato, D

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Wireless Communications Letters, 2020, 9, (12), pp. 2140-2143
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Hieu, NQ
dc.contributor.author	Hoang, DT
dc.contributor.author	Luong, NC
dc.contributor.author	Niyato, D
dc.date.accessioned	2021-02-23T19:26:26Z
dc.date.available	2021-02-23T19:26:26Z
dc.date.issued	2020-12-01
dc.identifier.citation	IEEE Wireless Communications Letters, 2020, 9, (12), pp. 2140-2143
dc.identifier.issn	2162-2337
dc.identifier.issn	2162-2345
dc.identifier.uri	http://hdl.handle.net/10453/146364
dc.description.abstract	© 2012 IEEE. This letter introduces an intelligent Real-time Dual-functional Radar-Communication (iRDRC) system for autonomous vehicles (AVs). This system enables an AV to perform both radar and data communications functions to maximize bandwidth utilization as well as significantly enhance safety. In particular, the data communications function allows the AV to transmit data, e.g., of current traffic, to edge computing systems and the radar function is used to enhance the reliability and reduce the collision risks of the AV, e.g., under bad weather conditions. The problem of the iRDRC is to decide when to use the communication mode or the radar mode to maximize the data throughput while minimizing the miss detection probability of unexpected events given the uncertainty of surrounding environment. To solve the problem, we develop a deep reinforcement learning algorithm that allows the AV to quickly obtain the optimal policy without requiring any prior information about the environment. Simulation results show that the proposed scheme outperforms baseline schemes in terms of data throughput, miss detection probability, and convergence rate.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Wireless Communications Letters
dc.relation.isbasedon	10.1109/LWC.2020.3014972
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.title	IRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-23T19:26:25Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	12

Abstract:

© 2012 IEEE. This letter introduces an intelligent Real-time Dual-functional Radar-Communication (iRDRC) system for autonomous vehicles (AVs). This system enables an AV to perform both radar and data communications functions to maximize bandwidth utilization as well as significantly enhance safety. In particular, the data communications function allows the AV to transmit data, e.g., of current traffic, to edge computing systems and the radar function is used to enhance the reliability and reduce the collision risks of the AV, e.g., under bad weather conditions. The problem of the iRDRC is to decide when to use the communication mode or the radar mode to maximize the data throughput while minimizing the miss detection probability of unexpected events given the uncertainty of surrounding environment. To solve the problem, we develop a deep reinforcement learning algorithm that allows the AV to quickly obtain the optimal policy without requiring any prior information about the environment. Simulation results show that the proposed scheme outperforms baseline schemes in terms of data throughput, miss detection probability, and convergence rate.

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http://hdl.handle.net/10453/146364