SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:research.chalmers.se:eb95bd65-8f12-4af3-8854-86a600865fa0"
 

Sökning: id:"swepub:oai:research.chalmers.se:eb95bd65-8f12-4af3-8854-86a600865fa0" > Model-Based Machine...

Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

Butler, Rick M. (författare)
Technische Universiteit Eindhoven,Eindhoven University of Technology
Häger, Christian, 1986 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Pfister, Henry D. (författare)
Duke University
visa fler...
Liga, Gabriele (författare)
Technische Universiteit Eindhoven,Eindhoven University of Technology
Alvarado, A. (författare)
Technische Universiteit Eindhoven,Eindhoven University of Technology
visa färre...
 (creator_code:org_t)
2021
2021
Engelska.
Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 39:4, s. 949-959
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy