将大型语言模型集成到流动天线系统:一项调查
Tingsong Deng1, Yan Gao2, Tong Zhang3,4
1School of Information Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
大型语言模型 (LLM) 可以增强无线网络的流体天线系统 (FAS). 在优化任务中,LLM增强的算法显示出显著的性能改善.
科学领域:
- 无线通信
- 人工智能
背景情况:
- 流体天线系统 (FAS) 为下一代无线网络提供动态重新配置.
- 在FAS的关键挑战包括通道估计和性能优化.
研究的目的:
- 调查大型语言模型 (LLM) 在应对FAS挑战中的应用.
- 探索基于LLM的道估计,位置优化和网络模拟方法.
- 调查LLM代理人在FAS管理中的作用.
主要方法:
- 对基于LLM的FAS通道估计技术的审查.
- 探索LLM辅助的流体天线位置优化.
- 讨论LLM支持的FAS网络模拟和LLM代理.
- 一个LLM增强的遗传算法的实验评估.
主要成果:
- 通过LLM增强的遗传算法显示了75.9%的性能改善.
- 对基于LLM的FAS通道估计和优化的重大进展进行了审查.
- 讨论了LLM代理人在管理复杂的FAS环境中的潜力.
结论:
- 在流体天线系统中克服关键问题,LLM提供了强大的工具包.
- 在未来的无线网络中,LLM集成有望提高性能和适应性.
- 需要对FAS管理的LLM代理进行进一步的研究.
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