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To find the trade-off between communication and sensing (C\\&S) in the system, we formulate a multi-objective optimization problem (MOP) to maximize the total network utility and the localization Cramér-Rao bounds (CRB) of ground users, which jointly optimizes the deployment and power control of UAVs. Inspired by the huge potential of large language models (LLM) for prediction and inference, we propose an LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP. We first adopt a decomposition-based scheme to decompose the MOP into a series of optimization sub-problems. We second integrate LLMs as black-box search operators with MOP-specifically designed prompt engineering into the framework of MOEA to solve optimization sub-problems simultaneously. Numerical results demonstrate that the proposed LEDMA can find the clear trade-off between C\\&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence."],"publicationDate":"2025-04-01","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","embargoEndDate":"2024-10-01","sources":["Crossref","IEEE Wireless Communications Letters"],"formats":["979 - 983","Print-Electronic","1 - 5"],"contributors":null,"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":{"name":"IEEE Wireless Communications 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