AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing

Chu, NH; Nguyen, DN; Hoang, DT; Pham, QV; Phan, KT; Hwang, WJ; Dutkiewicz, E

AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing

Chu, NH Nguyen, DN Hoang, DT Pham, QV Phan, KT Hwang, WJ Dutkiewicz, E

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Chu, NH
dc.contributor.author	Nguyen, DN
dc.contributor.author	Hoang, DT
dc.contributor.author	Pham, QV
dc.contributor.author	Phan, KT
dc.contributor.author	Hwang, WJ
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.date.accessioned	2023-08-27T08:56:12Z
dc.date.available	2023-08-27T08:56:12Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2023, PP, (99), pp. 1-1
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/171839
dc.description.abstract	Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DE210100651
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3270420
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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/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
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-04-26T00:00:00+1000Z
dc.date.updated	2023-08-27T08:56:11Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/171839