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一种两阶段时间域均等化方法,用于减轻单载波THz通信系统的非线性扭曲
Yunchuan Liu1,2,3,4, Hongcheng Yang1,2,3,4, Ziqi Liu1,2,3,4
1Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Beijing 100048, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
一个新的两阶段深度学习等分器显著降低了太赫兹 (THz) 通信系统的比特错误率. 这种方法有效地减轻非线性扭曲,提高未来无线网络的数据传输可靠性.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 太赫兹 (THz) 通信对于高速数据传输至关重要,因为它的带宽很大.
- 非线性扭曲和随机干扰降低了THz通信性能.
- 需要有效的均衡技术来提高可靠性.
研究的目的:
- 为单载波THz系统提出基于深度学习的两阶段时间域均等方法.
- 为了减轻非线性扭曲,提高通信可靠性.
- 验证方法的性能和概括能力.
主要方法:
- 一个两阶段的深度学习 (DL) 框架,用于时间域均等化.
- 一个渐进式的学习策略,捕捉全球,然后是本地道特征.
- 在230 GHz (2.1 m) 和310 GHz (1.5 m) 的实验验证.
主要成果:
- 实现了比特错误率 (BER) 的显著降低.
- 在230 GHz下降了大约92.15%的BER,在310 GHz下降了83.33%.
- 在不同的实验条件下表现出稳定的性能.
结论:
- 拟议的双阶段DL均衡器有效地减轻了THz通信中的非线性扭曲.
- 该方法为提高THz通信可靠性提供了一个有希望的解决方案.
- 这种方法支持未来的THz无线系统的研究和开发.
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