Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation

Mohannad Abu-Romoh, Nelson Costa, Yves Jaouën, Antonio Napoli, João Pedro +2 more
5/26/2023
cs.NIcs.LGeess.SP

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Abstract

In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmission system operating at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a 2016 km link with 15% residual dispersion. Our results show that in single-channel transmission, LDBP achieves an effective signal-to-noise ratio improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and DBP. In WDM transmission, the corresponding $Q$-factor gains are 1.1 dB and 0.4 dB, respectively. Additionally, we conduct a complexity analysis, which reveals that a frequency-domain implementation of LDBP and DBP is more favorable in terms of complexity than the time-domain implementation. These findings demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM fiber-optic transmission systems.

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Cite this paper

@article{aburomoh2023equalization,
  title  = {Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation},
  author = {Mohannad Abu-Romoh and Nelson Costa and Yves Jaouën and Antonio Napoli and João Pedro and Bernhard Spinnler and Mansoor Yousefi},
  year   = {2023},
  eprint = {2307.06821},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.06821v1}
}

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