Large Language Model-Based Wireless Network Design
In 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.
Authors
- Stefanos Bakirtzis (ORCID: https://orcid.org/0000-0002-7958-0495)
- Kehai Qiu (ORCID: https://orcid.org/0000-0003-0355-284X)
- Kezhi Wang (ORCID: https://orcid.org/0000-0001-8602-0800)
- Hui Song (ORCID: https://orcid.org/0000-0002-0458-5136)
- Ian Wassell (ORCID: https://orcid.org/0000-0001-7927-5565)
- Jie Zhang (ORCID: https://orcid.org/0000-0002-3354-0690)
Institutions
- University of Cambridge (GB)
- UK Power Networks (GB)
- Brunel University of London (GB)
Publication Details
- Journal
- IEEE Wireless Communications Letters
- Published
- 2024-09-17
- DOI
- https://doi.org/10.1109/lwc.2024.3462556
- Citations
- 22
- Primary Topic
- Advanced MIMO Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 4.59
Funders
- Royal Society
- Alexander S. Onassis Public Benefit Foundation
- Foundation for Education and European Culture