Experimental Validation of XPM Mitigation Using a Generalizable Multi-Task Learning Neural Network

We address the development of efficient neural networks (NN)-based post-equalizers in long-haul coherent-detection dense wavelength-division multiplexing optical transmission systems. To achieve a high level of generalization of the NN-based equalizers, we propose to employ multi-task learning (MTL). MTL refers to a single shared machine learning (NN) model that can perform multiple different (albeit related) tasks. We verify the good performance of the developed MTL equalizer model using experimental data as compared to the previously proposed approaches. Furthermore, we report how MTL can improve performance compared to single-task counterparts. We also demonstrate that reducing the complexity of the resulting MTL equalizer is possible without essential performance compromise.