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：数字子载波复用系统中光通道非线性补偿的神经网络结构
翻译后的摘要：
本文提出采用各种人工神经网络（ANN）结构来建模和补偿数字子载波复用（DSCM）光传输系统中子载波内部和跨子载波的光纤非线性干扰。我们通过采用不同的神经网络核心，包括卷积神经网络（CNN）和长短期记忆（LSTM）层来进行非线性信道均衡。我们通过在所有子载波之间使用全连接网络来抵消DSCM系统中的纤维非线性畸变。在随后的步骤中，并借鉴光纤非线性分析，我们逐渐升级设计，朝着性能复杂度优势更好的模块化结构发展。我们的研究表明，在DSCM系统中，将适当的宏结构纳入ANN非线性均衡器的设计可能对未来的相干光收发器的实际解决方案至关重要。
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