Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
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Abstract (arXiv)
We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2020.
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Citing papers per year: 2022: 4; 2023: 1.
5 papers.
- 2022Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systemscited by 4
- 2022FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Trainingcited by 2
- 2022Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systemscited by 1
- 2022Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmissioncited by 0
- 2023Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagationcited by 0
- 2023Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation26 May 2023
- 2022FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training7 Dec 2022
- 2022Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission11 Oct 2022
- 2022Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems24 Jun 2022
- 2022Learned Digital Back-Propagation for Dual-Polarization Dispersion Managed Systems23 May 2022