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.

Digital backpropagation

nearMAP detector

optical communications

factor graphs

nonlinear compensation

Author

Naga Vishnukanth Irukulapati

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

D. Marsella

Scuola Superiore Sant'Anna (SSSUP)

Nokia Oy

Pontus Johannisson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Erik Agrell

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

M. Secondini

Scuola Superiore Sant'Anna (SSSUP)

Henk Wymeersch

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

Journal of Lightwave Technology

0733-8724 (ISSN)

Vol. 34 2 566-572 7247642

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Communication Systems

Signal Processing

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

DOI

10.1109/JLT.2015.2477462

More information

Latest update

9/6/2018 2