mlsanityData to 14 Oct 2026

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

arXiv 2307.05374 ↗First posted 4 Jul 2023eess.SP cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 4 Jul 2023.

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6 of 17 references.

References · 6

  1. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 375
  2. 2017Nonlinear Interference Mitigation via Deep Neural Networkscited by 13
  3. 2020Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networkscited by 8
  4. 2021Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systemscited by 7
  5. 2022Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systemscited by 4
  6. 2022Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connectioncited by 3

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