mlsanityData to 7 Oct 2026

Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems

arXiv 2202.12689 ↗First posted 25 Feb 2022eess.SP cs.LGcited by 4

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Abstract (arXiv)

We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99% training process reduction, which we demonstrate in three experimental setups.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

5 of 10 references.

References · 5

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  2. 2020Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalizationcited by 92
  3. 2021Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systemscited by 17
  4. 2021Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systemscited by 7
  5. 2019Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensationcited by 4

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1.

4 papers.

  1. 2022Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementationcited by 6
  2. 2022Computational Complexity Evaluation of Neural Network Applications in Signal Processingcited by 1
  3. 2022Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrayscited by 1
  4. 2023Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systemscited by 0