Stochastic Digital Backpropagation With Residual Memory Compensation

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 t...

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Bibliographic Details
Published in:Journal of lightwave technology 2016-01, Vol.34 (2), p.566-572
Main Authors: Irukulapati, Naga V., Marsella, Domenico, Johannisson, Pontus, Agrell, Erik, Secondini, Marco, Wymeersch, Henk
Format: Article
Language:English
Subjects:
Approximation methods
Backpropagation
Complexity theory
Detectors
Digital backpropagation
factor graphs
near- MAP detector
nearMAP detector
Noise
nonlinear compensation
optical communications
Phase shift keying
Receivers
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Summary: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 nonoptimal 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.
ISSN:0733-8724
1558-2213
1558-2213
DOI:10.1109/JLT.2015.2477462