In this paper, we introduce a new nonlinear channel equalization method for the coherent long-haul transmission based on Transformers. We show that due to their capability to attend directly to the memory across a sequence of symbols, Transformers can be used effectively with a parallelized structure. We present an implementation of encoder part of Transformer for nonlinear equalization and analyze its performance over a wide range of different hyper-parameters. It is shown that by processing blocks of symbols at each iteration and carefully selecting subsets of the encoder's output to be processed together, an efficient nonlinear compensation can be achieved. We also propose the use of a physic-informed mask inspired by nonlinear perturbation theory for reducing the computational complexity of Transformer nonlinear equalization.

翻译：本文提出了一种基于Transformer的相干长距离传输非线性信道均衡方法。研究表明，由于Transformer能够直接关注符号序列中的记忆信息，可借助其并行化结构实现高效应用。我们实现了Transformer编码器部分的非线性均衡方案，并在广泛的不同超参数范围内分析其性能。结果表明，通过在每次迭代中处理符号块，并精心选择编码器输出子集进行联合处理，可实现高效的非线性补偿。此外，受非线性扰动理论启发，我们提出了一种物理信息掩码，用于降低Transformer非线性均衡的计算复杂度。