Leveraging Large Language Models for the Design and Optimization of Fluid Antenna Systems
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The paper investigates the potential of Large Language Models (LLMs) in optimizing Fluid Antenna Systems (FAS) for 6G networks. It addresses key challenges such as channel extrapolation, flexible precoder design, and cooperative FAS optimization through a novel LLM-driven framework and case studies, demonstrating significant performance gains.
Recommended citation: Wang C, Wong K K, Li Z, et al. Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G[J]. arXiv preprint arXiv:2506.14288, 2025.
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