Abstract
High speed optical transmission systems suffer from intersymbol interference (ISI) and colored noise induced by nonlinear bandwidth limited optical and electrical components. As a countermeasure, this article investigates deep neural network soft-demappers. In particular, we propose a bidirectional recurrent neural network soft-demapper (BRNN-SD) and benchmarked its performance against a time delay neural network soft-demapper (TDNN-SD) and a reference digital signal processing (DSP) scheme consisting of a Volterra nonlinear equalizer accompanied by a symbol-spaced whitening filter and a BCJR detector. On coherent 92GBd dual polarization (DP)-32QAM back-to-back measurements, the proposed soft-demapper matches the performance of the reference DSP. In 800 Gb/s 96GBd DP-32QAM 32-channel dense wavelength division multiplexing (DWDM) transmission over a 600 km fiber link the proposed approach outperforms the reference DSP.
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription