mlsanityData to 14 Oct 2026

Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems

arXiv 2206.12180 ↗First posted 24 Jun 2022eess.SP cs.LGcited by 4

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Abstract (arXiv)

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to that of a dispersion equalizer. We demonstrate that the NN-based equalizers can outperform a 1 step-per-span DBP.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 24 Jun 2022.

Builds on

7 of 14 references.

References · 7

  1. 2018An Overview on Application of Machine Learning Techniques in Optical Networkscited by 19
  2. 2021Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systemscited by 19
  3. 2020Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensationcited by 7
  4. 2021Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systemscited by 7
  5. 2020End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theorycited by 4
  6. 2021Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systemscited by 3
  7. 2022Mitigating Nonlinear Interference by Limiting Energy Variations in Sphere Shapingcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2022FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Trainingcited by 2
  2. 2023Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementationcited by 2
  3. 2023Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizerscited by 0
  4. 2024Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communicationscited by 0