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转移学习启用了基于变压器的生成对抗网络,用于建模和生成太赫兹频道
Zhengdong Hu1, Yuanbo Li1, Chong Han2
1Terahertz Wireless Communications (TWC) Laboratory, Shanghai Jiao Tong University, 200240, Shanghai, China.
Communications engineering
|November 3, 2024
概括
这项研究引入了用于太赫兹频道建模的新人工智能方法,减少了对广泛测量的需求. 转移学习支持的网络准确地模拟了使用较少数据的太赫兹通信.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 太赫兹 (THz) 通信是未来无线系统的关键,提供每秒 (Tbps) 千兆比特的数据速率.
- 准确的频道建模对于实现THz频谱潜力至关重要,但目前的方法需要广泛,昂贵的测量.
研究的目的:
- 开发一种高效准确的太赫兹频道建模技术.
- 为了减少依赖耗时和昂贵的物理测量在THz频道表征.
主要方法:
- 使用生成对抗网络 (GAN) 来生成通道参数.
- 为提高准确性,将一个具有自我注意机制的变压器结构集成到GAN中.
- 转移学习被应用来解决模型和现实世界测量之间的差异.
主要成果:
- 拟议的模型在太赫兹频道建模中实现了高精度.
- 该方法显著减少了所需的测量数据的数量.
- 这种方法证明了对传统的测量密集型技术的有希望的替代方案.
结论:
- 基于转移学习的基于变压器的GAN为太赫兹频道建模提供了高效的解决方案.
- 这种人工智能驱动的方法通过将测量需求降到最低来补充现有方法.
- 该技术为加速开发和部署6G和超越无线系统铺平了道路.
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