Yan Gao
Yan Gao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yan Gao has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Hao Xu
School of Future Transportation, Guangzhou Maritime University, Guangzhou, 510725, China.
1 shared publication - Mingjie Shao
Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
1 shared publication - Tingsong Deng
School of Information Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
1 shared publication - Tong Zhang
Nanjing University of Aeronautics and Astronautics
1 shared publication - Wanli Ni
Key Laboratory of Universal Wireless Communications, Ministry of Education, and the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
1 shared publication
Latest Publications
- Open Access
Integrating Large Language Models into Fluid Antenna Systems: A Survey
Fluid antenna system (FAS) has emerged as a promising technology for next-generation wireless networks, offering dynamic reconfiguration capabilities to adapt to varying channel conditions. However, FAS faces critical issues from channel estimation to performance optimization. This paper provides a survey of a how large language model (LLM) can be leveraged to address these issues. We review potential approaches and recent advancements in LLM-based FAS channel estimation, LLM-assisted fluid antenna position optimization, and LLM-enabled FAS network simulation. Furthermore, we discuss the role of LLM agents in FAS management. As an experimental study, we evaluated the performance of our designed LLM-enhanced genetic algorithm. The results demonstrated a performance improvement over the traditional genetic algorithm on the Rastrigin function.Open AccessIntegrating Large Language Models into Fluid Antenna Systems: A Survey
Fluid antenna system (FAS) has emerged as a promising technology for next-generation wireless networks, offering dynamic reconfiguration capabilities to adapt to varying channel conditions. However, FAS faces critical issues from channel estimation to performance optimization. This paper provides a survey of a how large language model (LLM) can be leveraged to address these issues. We review potential approaches and recent advancements in LLM-based FAS channel estimation, LLM-assisted fluid antenna position optimization, and LLM-enabled FAS network simulation. Furthermore, we discuss the role of LLM agents in FAS management. As an experimental study, we evaluated the performance of our designed LLM-enhanced genetic algorithm. The results demonstrated a performance improvement over the traditional genetic algorithm on the Rastrigin function.