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Cost-Gain Analysis of Sequence Selection for Nonlinearity Mitigation

Stella Civelli, Marco Secondini

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.

Cost-Gain Analysis of Sequence Selection for Nonlinearity Mitigation

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.

Paper Structure

This paper contains 4 sections, 1 equation, 1 figure.

Table of Contents

  1. Introduction
  2. Sequence Selection
  3. Performance Analysis
  4. Conclusion

Figures (1)

  • Figure 1: SE vs (a) number of tested sequences $N_{t}$ for different numbers of CB-ESSFM steps $N_{\mathrm{st}}$; (b) total computational cost for $N_{t}$ metric evaluations (with different values of $N_{t}$).