Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/33136
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dc.contributor.authorShi, Q-
dc.contributor.authorLiu, M-
dc.contributor.authorZhou, Y-
dc.contributor.authorZhou, Z-
dc.contributor.authorFan, P-
dc.date.accessioned2026-04-12T11:19:24Z-
dc.date.available2026-04-12T11:19:24Z-
dc.date.issued2026-02-17-
dc.identifierORCiD: Yi Zhou https://orcid.org/0000-0001-6407-068X-
dc.identifier.citationShi, Q. et al. (2026) 'Waveform Design for MIMO Covert Sensing and Communications', IEEE Transactions on Vehicular Technology, 0 (early access), pp. 1–6. doi: 10.1109/tvt.2026.3665719.en-US
dc.identifier.issn0018-9545-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/33136-
dc.description.abstractIn this paper, we consider a multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) system, aiming at simultaneously communicating with multiple users and sensing targets that serve as adversarial observers, under multiple signal-dependent interference sources. A novel concept of covert sensing, preventing adversarial observers from detecting our sensing action or sensing intentions, is proposed, and a new metric named covert sensing distance (CSD) is established to evaluate the corresponding performance. Specifically, we develop a two-stage scheme. The first stage is to minimize the multi-user interference (MUI) while designing an omnidirectional transmit beampattern by constraining the CSD, to guarantee both reliable communications and covert sensing. Leveraging receiver design flexibility, the second stage formulates a trade-off optimization problem by maximizing the sensing signal-to-interference-plus-noise ratio (SINR) and imposing a similarity constraint to obtain a directional receive beampattern to improve the sensing performance. To solve the formulated nonconvex problem, we propose an efficient alternating optimization algorithm aided by the gradient-projection framework. Finally, the effectiveness of the proposed scheme is validated by simulation results.en-US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grants U23A20274 and 62301462, in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1418, in part by the China Postdoctoral Science Foundation under Grant 2023M742901, in part by UKRI Postdoc Guarantee project S-ISAC [grant number EP/Z002435/1] and EU MSCA Postdoctoral Fellowships [grant number 101154926].en-US
dc.format.extent1 - 6-
dc.format.mediumPrint-Electronic-
dc.languageen-USen-US
dc.language.isoenen-US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en-US
dc.rightsCreative Commons Attribution 4.0 International-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectmultiple-input multiple-outputen-US
dc.subjectintegrated sensing and communicationsen-US
dc.subjectcovert sensing distanceen-US
dc.subjectwaveform designen-US
dc.subjectmulti-user interferenceen-US
dc.titleWaveform Design for MIMO Covert Sensing and Communicationsen-US
dc.typeArticleen-US
dc.identifier.doihttps://doi.org/10.1109/tvt.2026.3665719-
dc.relation.isPartOfIEEE Transactions on Vehicular Technology-
pubs.issueearly access-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn1939-9359-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dc.rights.holderThe Author(s)-
dc.contributor.orcidZhou, Yi [0000-0001-6407-068X]-
Appears in Collections:Department of Computer Science Research Papers

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