Equalization Methods for NLIN Mitigation

TL;DR

This paper explores adaptive equalization techniques, including Kalman filtering and MLSE, to mitigate nonlinear interference noise in communication systems, demonstrating significant performance improvements over traditional methods.

Contribution

It introduces a novel adaptive equalization scheme leveraging NLIN statistics, Kalman filtering, and MLSE for improved mitigation of nonlinear interference noise.

Findings

Adaptive equalization reduces NLIN impact.

Proposed scheme outperforms traditional equalizers.

Significant performance gains demonstrated.

Abstract

We investigate the potential of adaptive equalization techniques to mitigate inter-channel nonlinear interference noise (NLIN). We derive a lower bound on the mutual information of a system using adaptive equalization, showing that the channel estimation error determines the equalizer's performance. We develop an adaptive equalization scheme which uses the statistics of the NLIN to obtain optimal detection, based on Kalman filtering and maximum likelihood sequence estimation (MLSE). This scheme outperforms commonly used equalizers and significantly increases performance.