Revealing the Trade-Off in ISAC Systems: The KL Divergence Perspective

Fei, Z; Tang, S; Wang, X; Xia, F; Liu, F; Zhang, JA

Revealing the Trade-Off in ISAC Systems: The KL Divergence Perspective

Fei, Z Tang, S Wang, X Xia, F Liu, F Zhang, JA

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Wireless Communications Letters, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Fei, Z
dc.contributor.author	Tang, S
dc.contributor.author	Wang, X
dc.contributor.author	Xia, F
dc.contributor.author	Liu, F
dc.contributor.author	Zhang, JA
dc.date.accessioned	2024-09-05T11:05:44Z
dc.date.available	2024-09-05T11:05:44Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Wireless Communications Letters, 2024, PP, (99), pp. 1-1
dc.identifier.issn	2162-2337
dc.identifier.issn	2162-2345
dc.identifier.uri	http://hdl.handle.net/10453/180715
dc.description.abstract	Integrated sensing and communication (ISAC) is regarded as a promising technique for sixth-generation (6G) communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler divergence (KLD) performance measure. We firstly use KLD to analyze the detection performance of communication and sensing in the ISAC system. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection (PD) under different parameter settings.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Wireless Communications Letters
dc.relation.isbasedon	10.1109/LWC.2024.3443409
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Revealing the Trade-Off in ISAC Systems: The KL Divergence Perspective
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Global Big Data Technologies Research Centre (GBDTC)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-05T11:05:42Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Integrated sensing and communication (ISAC) is regarded as a promising technique for sixth-generation (6G) communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler divergence (KLD) performance measure. We firstly use KLD to analyze the detection performance of communication and sensing in the ISAC system. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection (PD) under different parameter settings.

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http://hdl.handle.net/10453/180715