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Model-Based Machine...
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
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- Butler, Rick M. (författare)
- Technische Universiteit Eindhoven,Eindhoven University of Technology
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- Häger, Christian, 1986 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Pfister, Henry D. (författare)
- Duke University
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- Liga, Gabriele (författare)
- Technische Universiteit Eindhoven,Eindhoven University of Technology
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- Alvarado, A. (författare)
- Technische Universiteit Eindhoven,Eindhoven University of Technology
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(creator_code:org_t)
- 2021
- 2021
- Engelska.
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 39:4, s. 949-959
- Relaterad länk:
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- In this article, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The resulting method combines hardware-friendly time-domain nonlinearity mitigation via the recently proposed learned digital backpropagation (LDBP) with distributed compensation of polarization-mode dispersion (PMD). We refer to the resulting approach as LDBP-PMD. We train LDBP-PMD on multiple PMD realizations and show that it converges within 1% of its peak dB performance after 428 training iterations on average, yielding a peak effective signal-to-noise ratio of only 0.30 dB below the PMD-free case. Similar to state-of-the-art lumped PMD compensation algorithms in practical systems, our approach does not assume any knowledge about the particular PMD realization along the link, nor any knowledge about the total accumulated PMD. This is a significant improvement compared to prior work on distributed PMD compensation, where knowledge about the accumulated PMD is typically assumed. We also compare different parameterization choices in terms of performance, complexity, and convergence behavior. Lastly, we demonstrate that the learned models can be successfully retrained after an abrupt change of the PMD realization along the fiber.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Deep learning
- polarization-mode dispersion
- machine learning
- optical fiber communications
- digital signal processing
- dual-polarization transmission
- digital backpropagation
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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