For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.

翻译：首次提出利用多任务学习提升相干系统中基于神经网络的均衡器的灵活性。与色散补偿模块相比，单一神经网络均衡器在发射功率、符号速率或传输距离变化的情况下，无需重新训练即可将Q因子提升高达4 dB。