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

TL;DR

This paper explores the use of learned digital back-propagation (LDBP), a neural network-based method, to improve equalization in dispersion-managed fiber-optic systems, showing significant SNR and Q-factor gains over traditional methods.

Contribution

The paper introduces LDBP as a novel neural network approach for fiber-optic equalization, demonstrating its effectiveness and complexity advantages over conventional digital back-propagation.

Findings

LDBP improves SNR by 6.3 dB over linear equalization.

LDBP achieves a 2.5 dB SNR gain over traditional DBP.

In WDM systems, LDBP provides 1.1 dB Q-factor improvement.

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.