Low-Complexity Pruned Convolutional Neural Network Based Nonlinear Equalizer in Coherent Optical Communication Systems

Abstract

Nonlinear impairments caused by devices and fiber transmission links in a coherent optical communication system can severely limit its transmission distance and achievable capacity. In this paper, we propose a low-complexity pruned-convolutional-neural-network-(CNN)-based nonlinear equalizer, to compensate nonlinear signal impairments for coherent optical communication systems. By increasing the size of the effective receptive field with an 11 × 11 large convolutional kernel, the performance of feature extraction for CNNs is enhanced and the structure of the CNN is simplified. And by performing the channel-level pruning algorithm, to prune the insignificant channels, the complexity of the CNN model is dramatically reduced. These operations could save the important component of the CNN model and reduce the model width and computation amount. The performance of the proposed CNN-based nonlinear equalizer was experimentally evaluated in a 120 Gbit/s 64-quadrature-amplitude-modulation (64-QAM) coherent optical communication system over 375 km of standard single-mode fiber (SSMF). The experimental results showed that, compared to a CNN-based nonlinear equalizer with a 6 × 6 normal convolutional kernel, the proposed CNN-based nonlinear equalizer with an 11 × 11 large convolutional kernel, after channel-level pruning, saved approximately 15.5% space complexity and 43.1% time complexity, without degrading the equalization performance. The proposed low-complexity pruned-CNN-based nonlinear equalizer has great potential for application in realistic devices and holds promising prospects for coherent optical communication systems.