Multi-task learning to enhance generalizability of neural network equalizers in coherent optical systems

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.