Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

2023/07/04 by Sasipim Srivallapanondh, Srivallapanondh, Sasipim, Pedro J. Freire +15
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Optical Network Technologies #Photonic and Optical Devices #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2307.05374

openalex publication_date 2023/07/04 · openalex created_date 2023/07/13 · openalex updated_date 2026/07/28

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.

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