Improved Lower Bounds on Mutual Information Accounting for Nonlinear Signal-Noise Interaction

TL;DR

This paper introduces a novel approach for computing lower bounds on mutual information in fiber-optic channels using auxiliary backward channels, demonstrating improved bounds with stochastic digital backpropagation that accounts for nonlinear signal-noise interactions.

Contribution

It proposes using auxiliary backward channels for mutual information bounds and applies stochastic digital backpropagation to better account for nonlinear effects in fiber-optic communications.

Findings

Higher information rates achieved with SDBP

SDBP accounts for nonlinear signal-noise interactions

Backward channel approach improves MI bounds

Abstract

In fiber-optic communications, evaluation of mutual information (MI) is still an open issue due to the unavailability of an exact and mathematically tractable channel model. Traditionally, lower bounds on MI are computed by approximating the (original) channel with an auxiliary forward channel. In this paper, lower bounds are computed using an auxiliary backward channel, which has not been previously considered in the context of fiber-optic communications. Distributions obtained through two variations of the stochastic digital backpropagation (SDBP) algorithm are used as auxiliary backward channels and these bounds are compared with bounds obtained through the conventional digital backpropagation (DBP). Through simulations, higher information rates were achieved with SDBP, {which can be explained by the ability of SDBP to account for nonlinear signal--noise interactions