Equalization in Dispersion-Managed SystemsUsing Learned Digital Back-Propagation

IF 1.1 Q4 OPTICS
Mohannad Abu-Romoh, Nelson Costa, Yves Jaouën, Antonio Napoli, João Pedro, Bernhard Spinnler, Mansoor Yousefi
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引用次数: 0

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 32 Gbaud/s per channel, with a dispersion map designed for a 28 × 72 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 using DP-16-QAM format, 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.
基于学习数字反向传播的分散管理系统均衡化
在本文中,我们研究了在色散管理(DM)链路中使用学习数字反向传播(LDBP)来均衡双偏振光纤传输。LDBP是一种深度神经网络,它利用随机梯度下降法对DBP参数进行优化。我们在模拟的WDM双偏振光纤传输系统中评估了DBP和LDBP,该系统工作在每通道32 Gbaud/s,色散图设计用于28 × 72 km链路,剩余色散为15%。我们的研究结果表明,在单通道传输中,使用DP-16-QAM格式的LDBP比线性均衡和DBP分别实现了6.3 dB和2.5 dB的有效信噪比提高。在WDM传输中,相应的Q因子增益分别为1.1 dB和0.4 dB。此外,我们进行了复杂性分析,结果表明LDBP和DBP的频域实现在复杂性方面比时域实现更有利。这些发现证明了LDBP在缓解DM光纤传输系统中的非线性效应方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
3.50
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