基于神经网络的无载波波幅相调节信号生成和端到端优化,用于光纤-泰拉赫兹集成通信系统
Optics express
|April 4, 2024
概括
本研究介绍了一种神经网络方法,用于在光纤-泰拉赫兹通信中预补偿信号,从而提高传输性能. 这种方法提高了信号质量,并减少了50 Gbps系统的复杂性.
科学领域:
- 光学通信是指光学通信.
- 特拉赫兹技术的技术
- 人工智能在通信中的应用
背景情况:
- 光纤-泰拉赫兹集成通信系统面临由于非线性扭曲和符号间干扰 (ISI) 的性能下降.
- 预补偿是减轻通道扭曲,避免噪声放大和简化接收端处理的重要技术,特别是在不对称访问场景中.
研究的目的:
- 提出和实验证明一种基于神经网络 (NN) 的方法,用于在光纤-泰拉赫兹集成通信中生成和优化无载波幅相 (CAP) 调制信号.
- 通过使用发射器预补偿信号,在接收器上实现简单的线性数字信号处理 (DSP).
主要方法:
- 一个神经网络 (NN) 用于从二次振幅调制符号直接生成CAP信号.
- 基于NN的发射器学习过器,同时产生,升级转换和预补偿信号.
- 对光纤-泰拉赫兹集成通信系统进行了NN的端到端优化.
主要成果:
- 在220 GHz运行的光纤特拉赫兹集成访问系统的实验演示.
- 在50 Gbps的传输速度下,获得了1.2 dB的灵敏度增益.
- 在10公里光纤传输和1米无线传输后,对前进错误纠正 (FEC) 位错误率 (BER) 1 × 10-2的值进行了性能评估.
结论:
- 拟议的基于NN的预补偿方法有效地解决了光纤-特拉赫兹系统中的非线性扭曲和ISI.
- 该系统表现出更好的灵敏度,并保持了光纤和无线连接的性能.
- 这种方法为未来高速集成通信网络的高效信号处理提供了有希望的解决方案.
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