Joint Communications and Sensing Employing Optimized MIMO-OFDM Signals

Wu, K; Zhang, JA; Ni, Z; Huang, X; Guo, YJ; Chen, S

Joint Communications and Sensing Employing Optimized MIMO-OFDM Signals

Wu, K

Zhang, JA Ni, Z

Huang, X

Guo, YJ Chen, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (6), pp. 10368-10383
Issue Date:: 2024-03-15

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Field	Value	Language
dc.contributor.author	Wu, K https://orcid.org/0000-0003-1298-6404
dc.contributor.author	Zhang, JA
dc.contributor.author	Ni, Z https://orcid.org/0000-0002-7932-5996
dc.contributor.author	Huang, X https://orcid.org/0000-0001-6869-5732
dc.contributor.author	Guo, YJ
dc.contributor.author	Chen, S
dc.date.accessioned	2024-04-22T11:16:37Z
dc.date.available	2024-04-22T11:16:37Z
dc.date.issued	2024-03-15
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (6), pp. 10368-10383
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/178200
dc.description.abstract	Joint communications and sensing (JCAS) have the potential to improve the overall energy, cost and frequency efficiency of Internet of Things (IoT) systems. As a first effort, we propose to optimize the MIMO-OFDM data symbols carried by subcarriers (SCs) for better time- and spatial-domain signal orthogonality. This can reduce intertarget and interantenna interference, enabling high-quality sensing. We establish an optimization problem that modifies data symbols on SCs to enhance the above-mentioned signal orthogonality. We also develop an efficient algorithm to solve the problem based on the majorization-minimization framework. Moreover, we discover unique signal structures and features from the newly modeled problem, which substantially reduce the complexity of majorizing the objective function. We also develop new projectors to enforce the feasibility of the obtained solution. Simulations show that to achieve the same sensing performance, the optimized waveform can reduce the signal-to-noise ratio (SNR) requirement by 3-4.5 dB compared with the original waveform, while the SNR loss for the uncoded bit error rate is only 1-1.5 dB.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP210101411
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3327989
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	Joint Communications and Sensing Employing Optimized MIMO-OFDM Signals
dc.type	Journal Article
utslib.citation.volume	11
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	2026-03-15T00:00:00+1000Z
dc.date.updated	2024-04-22T11:16:35Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	6

Abstract:

Joint communications and sensing (JCAS) have the potential to improve the overall energy, cost and frequency efficiency of Internet of Things (IoT) systems. As a first effort, we propose to optimize the MIMO-OFDM data symbols carried by subcarriers (SCs) for better time- and spatial-domain signal orthogonality. This can reduce intertarget and interantenna interference, enabling high-quality sensing. We establish an optimization problem that modifies data symbols on SCs to enhance the above-mentioned signal orthogonality. We also develop an efficient algorithm to solve the problem based on the majorization-minimization framework. Moreover, we discover unique signal structures and features from the newly modeled problem, which substantially reduce the complexity of majorizing the objective function. We also develop new projectors to enforce the feasibility of the obtained solution. Simulations show that to achieve the same sensing performance, the optimized waveform can reduce the signal-to-noise ratio (SNR) requirement by 3-4.5 dB compared with the original waveform, while the SNR loss for the uncoded bit error rate is only 1-1.5 dB.

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http://hdl.handle.net/10453/178200