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离散频谱非线性频率分割复杂化系统融合了数字频率偏移加载与基于神经网络的接收器.
Optics express
|August 13, 2025
概括
一种新的数字频率偏移 (DFO) 加载技术与神经网络 (NN) 接收器相结合,可以增强离散频谱非线性频率分裂多重复合 (DS-NFDM) 系统. 这种DFO-NN系统显著降低了放大自发发射 (ASE) 噪声和处理错误,提高了传输性能.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 机器学习在电信中的应用
背景情况:
- 放大自发发射 (ASE) 噪声和处理错误降低了离散频谱非线性频率分割复杂化 (DS-NFDM) 系统中的信号完整性.
- 传统的非线性里叶变换 (NFT) 算法和频率偏移估计 (FOE) 方法在高功率DS-NFDM传输中与噪声和计算复杂性作斗争.
研究的目的:
- 提出和验证一个创新的DS-NFDM系统,称为DFO-NN,它将数字频率偏移 (DFO) 加载与神经网络 (NN) 接收器集成在一起.
- 减轻ASE噪声和抑制处理噪声,从而提高DS-NFDM系统中的信号质量和传输距离.
主要方法:
- 在发射机上实施了DFO加载技术,利用信息位和固有值偏移之间的映射来抵消ASE噪声.
- 采用优化的基于NN的接收器来学习DFO加载的DS-NFDM波形的周期性,取代传统的NFT和FOE过程.
- 通过数值模拟和在1 GBaud和2 GBaud的实验设置来验证系统的性能.
主要成果:
- DFO-NN系统显示了广泛的频率偏移容忍,范围从-5.8 GHz到+5.3 GHz.
- 在光信号与噪声比率 (OSNR) 处罚方面实现了显著的减少:与b-NFT相比6.0dB,与DFO-NFT系统相比2.0dB.
- 与DFO-NFT系统相比,最大传输距离延长了473公里,同时保持了可比的计算复杂性 (O(M^2).
结论:
- 拟议的DFO-NN系统有效地抑制了ASE噪音和DS-NFDM系统中的处理错误.
- 基于NN的接收器为DS-NFDM信号解调提供了传统NFT和FOE算法的强大而高效的替代方案.
- DFO-NN系统为扩展高功率光纤通信系统的覆盖范围和提高性能提供了一个有希望的解决方案.
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