We investigate the complexity and performance of recurrent neural network
(RNN) models as post-processing units for the compensation of fibre
nonlinearities in digital coherent systems carrying polarization multiplexed
16-QAM and 32-QAM signals. We evaluate three bi-directional RNN models, namely
the bi-LSTM, bi-GRU and bi-Vanilla-RNN and show that all of them are promising
nonlinearity compensators especially in dispersion unmanaged systems. Our
simulations show that during inference the three models provide similar
compensation performance, therefore in real-life systems the simplest scheme
based on Vanilla-RNN units should be preferred. We compare bi-Vanilla-RNN with
Volterra nonlinear equalizers and exhibit its superiority both in terms of
performance and complexity, thus highlighting that RNN processing is a very
promising pathway for the upgrade of long-haul optical communication systems
utilizing coherent detection.

研究了循环神经网络模型作为后处理单元用于补偿数字相干系统中的光纤非线性的复杂性和性能，并评估了三种双向循环神经网络模型，发现它们在特别是在未受扰动干扰管理系统中都是很有前途的非线性补偿器。模拟表明，在推断期间，这三个模型提供类似的补偿性能，因此，在现实生活中的系统中，应选择基于 Vanilla-RNN 单元的最简单的方案。与 Volterra 非线性均衡器进行比较并展示了它在性能和复杂性方面的优越性，因此凸显了 RNN 处理是升级利用相干检测的长途光通信系统的非常有前途的途径。