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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29784
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dc.contributor.authorQiu, K-
dc.contributor.authorBakirtzis, S-
dc.contributor.authorWassell, I-
dc.contributor.authorSong, H-
dc.contributor.authorZhang, J-
dc.contributor.authorWang, K-
dc.date.accessioned2024-09-21T07:14:25Z-
dc.date.available2024-09-21T07:14:25Z-
dc.date.issued2024-09-17-
dc.identifierORCiD: Kehai Qiu https://orcid.org/0000-0003-0355-284X-
dc.identifierORCiD: Stefanos Bakirtzis https://orcid.org/0000-0002-7958-0495-
dc.identifierORCiD: Ian Wassell https://orcid.org/0000-0001-7927-5565-
dc.identifierORCiD: Jie Zhang https://orcid.org/0000-0002-3354-0690-
dc.identifierORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800-
dc.identifier.citationQiu, K. et al. (2024) 'Large Language Model-Based Wireless Network Design', IEEE Wireless Communications Letters, 13 (12), pp. 3340 - 3344. doi: 10.1109/LWC.2024.3462556.en_US
dc.identifier.issn2162-2337-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/29784-
dc.description.abstractIn this letter, we present the Large Language Model-based combinatorial optimizer (LMCO) for wireless network optimization and planning tasks, focusing on optimizing the number and the placement of wireless access point placement. The performance and efficiency of LMCO are evaluated and compared with the well-established Ant Colony Optimization (ACO) algorithm. The results indicate that LMCO exhibits superior performance, particularly as the complexity of the problem increases. These findings also underscore the significant potential of LLM-based algorithms in revolutionizing combinatorial optimization across a wide range of applications.en_US
dc.description.sponsorship10.13039/501100005302-Alexander S. Onassis Public Benefit Foundation; 10.13039/501100005411-Foundation for Education and European Culture; Royal Society Industry Fellow Scheme (Grant Number: IF-R2-23200104); 10.13039/501100000288-Royal Society Project (Grant Number: IEC-NSFC-211264).en_US
dc.format.extent3340 - 3344-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2024 Institute of Electrical and Electronics Engineers (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 by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectlarge language modelen_US
dc.subjectnetwork optimizationen_US
dc.subjectcombinatorial optimizationen_US
dc.subjectaccess point placementen_US
dc.titleLarge Language Model-Based Wireless Network Designen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/LWC.2024.3462556-
dc.relation.isPartOfIEEE Wireless Communications Letters-
pubs.issue12-
pubs.publication-statusPublished-
pubs.volume13-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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