In this work, we propose to use various artificial neural network (ANN) structures for modeling and compensation of intra- and inter-subcarrier fiber nonlinear interference in digital subcarrier multiplexing (DSCM) optical transmission systems. We perform nonlinear channel equalization by employing different ANN cores including convolutional neural networks (CNN) and long short-term memory (LSTM) layers. We start to compensate the fiber nonlinearity distortion in DSCM systems by a fully connected network across all subcarriers. In subsequent steps, and borrowing from fiber nonlinearity analysis, we gradually upgrade the designs towards modular structures with better performance-complexity advantages. Our study shows that putting proper macro structures in design of ANN nonlinear equalizers in DSCM systems can be crucial for practical solutions in future generations of coherent optical transceivers.

翻译：在本文中，我们提出应用多种人工神经网络（ANN）结构，对数字子载波复用（DSCM）光传输系统中的子载波内及子载波间光纤非线性干扰进行建模与补偿。我们采用包含卷积神经网络（CNN）和长短期记忆（LSTM）层等不同ANN核心实现非线性信道均衡。首先，通过跨所有子载波的全连接网络补偿DSCM系统中的光纤非线性失真。随后，借鉴光纤非线性分析，逐步将设计升级为具有更优性能-复杂度权衡的模块化结构。研究表明，在DSCM系统的ANN非线性均衡器设计中引入合理的宏结构，对实现未来相干光收发机的实用化方案至关重要。