Cost-Gain Analysis of Sequence Selection for Nonlinearity Mitigation

TL;DR

This paper introduces a low-complexity metric for sequence selection to mitigate nonlinearity in communication systems, providing a benchmark for future improvements with a focus on balancing gain and computational cost.

Contribution

It proposes a sign-dependent metric for sequence selection and analyzes the nonlinear shaping gain achievable within a given computational complexity.

Findings

Small gains with feasible complexity

Higher gains require high complexity or advanced metrics

Establishes a benchmark for future research

Abstract

We propose a low-complexity sign-dependent metric for sequence selection and study the nonlinear shaping gain achievable for a given computational cost, establishing a benchmark for future research. Small gains are obtained with feasible complexity. Higher gains are achievable in principle, but with high complexity or a more sophisticated metric.