Channel Matching: An Adaptive Technique to Increase the Accuracy of Soft Decisions

TL;DR

This paper introduces an adaptive channel matching technique that compensates for nonlinear interference in time-varying Gaussian channels, significantly improving decoding accuracy over static models.

Contribution

It proposes a novel adaptive method to mitigate performance loss caused by approximating nonlinear, time-varying channels with static models.

Findings

Adaptive matching improves decoding accuracy in nonlinear channels

Performance loss from static approximation is significantly reduced

Method is effective for time-varying Gaussian channels

Abstract

Nonlinear interference is modeled by a time-varying conditionally Gaussian channel. It is shown that approximating this channel with a time-invariant channel imposes considerable loss in the performance of channel decoding. An adaptive method to maintain decoding performance is described.