In this paper, we introduce a new nonlinear optical channel equalizer based on Transformers. By leveraging parallel computation and attending directly to the memory across a sequence of symbols, we show that Transformers can be used effectively for nonlinear compensation (NLC) in coherent long-haul transmission systems. For this application, we present an implementation of the encoder part of the Transformer and analyze its performance over a wide range of different hyper-parameters. It is shown that by proper embeddings and processing blocks of symbols at each iteration and also carefully selecting subsets of the encoder's output to be processed together, an efficient nonlinear equalization can be achieved for different complexity constraints. To reduce the computational complexity of the attention mechanism, we further propose the use of a physic-informed mask inspired by nonlinear perturbation theory. We also compare the Transformer-NLC with digital back-propagation (DBP) under different transmission scenarios in order to demonstrate the flexibility and generalizability of the proposed data-driven solution.

翻译：本文提出了一种基于Transformer的新型非线性光信道均衡器。通过利用并行计算并直接关注符号序列中的记忆效应，我们证明了Transformer可有效应用于相干长距离传输系统中的非线性补偿。针对该应用，我们实现了Transformer的编码器部分，并分析了其在广泛超参数范围内的性能表现。研究表明，通过适当的嵌入方式、迭代处理符号块，并精心选择编码器输出中需协同处理的子集，可在不同复杂度约束下实现高效的非线性均衡。为降低注意力机制的计算复杂度，我们进一步提出采用受非线性扰动理论启发的物理信息掩码。为验证所提数据驱动方案的灵活性与泛化能力，我们在多种传输场景下将Transformer-NLC与数字反向传播进行了对比分析。