Stochastic Digital Backpropagation with Residual Memory Compensation
Journal article, 2016
Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.
factor graphs
nonlinear compensation
nearMAP detector
optical communications
Digital backpropagation
Author
Naga Vishnukanth Irukulapati
Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner
D. Marsella
Nokia Corporation
Scuola Superiore Sant'Anna di Studi Universitari e di Perfezionamento
Pontus Johannisson
Chalmers, Microtechnology and Nanoscience (MC2), Photonics
Erik Agrell
Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner
M. Secondini
Scuola Superiore Sant'Anna di Studi Universitari e di Perfezionamento
Henk Wymeersch
Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner
Journal of Lightwave Technology
0733-8724 (ISSN)
Vol. 34 2 566-572 7247642Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2011)
Telecommunications
Communication Systems
Signal Processing
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
DOI
10.1109/JLT.2015.2477462