Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
arXiv:2307.05374
Abstract
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.
4 pages, European Conference on Optical Communication (ECOC)