Stochastic Digital Backpropagation with Residual Memory Compensation

TL;DR

This paper enhances stochastic digital backpropagation (SDBP) by incorporating residual memory, significantly reducing symbol error rates in optical communication systems through novel algorithms like Viterbi and decision-directed approaches.

Contribution

It introduces residual memory compensation methods in SDBP, notably the Viterbi algorithm and decision-directed approach, improving error performance over previous symbol-by-symbol techniques.

Findings

Memory-based SDBP achieves lower SER than SBS-SDBP.

VA-SDBP reduces SER by up to 10-14 times compared to DBP.

Significant performance gains in dispersion-managed links for QPSK and 16-QAM.

Abstract

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.