A layer-reduced neural network based digital backpropagation algorithm called smoothing learned digital backpropagation (smoothing-LDBP), is proposed in this paper. The smoothing-LDBP smooths the power terms in nonlinear activation functions to limit the bandwidth. The limited bandwidth of the power terms generates fewer in-band distortions, thus reduces the required layer for a given equalization performance. Simulation results show that the required layers of smoothing-LDBP are reduced by approximately 62% at 6.7% HD-FEC compared with learned digital backpropagation. Owing to the layer reduction, the latency and the complexity are reduced by 69% and 51%, respectively. The layer-reduced property of smoothing-LDBP is also validated by a proof-of-concept experiment.

中文翻译：

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一种基于减层神经网络的数字反向传播算法，用于减轻光纤非线性


本文提出了一种基于层数减少的神经网络的数字反向传播算法，称为平滑学习数字反向传播（smoothing-LDBP）。平滑LDBP对非线性激活函数中的幂项进行平滑以限制带宽。功率项的有限带宽产生较少的带内失真，从而减少给定均衡性能所需的层数。仿真结果表明，与学习的数字反向传播相比，在 6.7% HD-FEC 下所需的平滑 LDBP 层数减少了约 62%。由于层数减少，延迟和复杂度分别降低了 69% 和 51%。平滑 LDBP 的层数减少特性也通过概念验证实验得到了验证。