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Stochastic Digital Backpropagation with Residual Memory Compensation

Naga VishnuKanth Irukulapati (Institutionen för signaler och system, Kommunikationssystem) ; Domenico Marsella ; Pontus Johannisson (Institutionen för mikroteknologi och nanovetenskap, Fotonik) ; Erik Agrell (Institutionen för signaler och system, Kommunikationssystem) ; Marco Secondini ; Henk Wymeersch (Institutionen för signaler och system, Kommunikationssystem)
Journal of Lightwave Technology (0733-8724). Vol. 34 (2016), 2, p. 566-572.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Digital backpropagation, factor graphs, nearMAP detector, nonlinear compensation, optical communications



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Denna post skapades 2015-10-14. Senast ändrad 2016-06-27.
CPL Pubid: 224273

 

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