End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties

TL;DR

This paper introduces a geometrically optimized constellation shape for optical communication that is robust against channel uncertainties, using end-to-end learning with realistic noise models, outperforming standard schemes.

Contribution

It presents a novel end-to-end learning approach to design constellation shapes resilient to channel uncertainties in optical networks, incorporating realistic noise models.

Findings

Learned constellations outperform standard QAM in robustness.

The approach is effective under both Gaussian and nonlinear noise models.

Constellations show improved transmission quality under uncertain conditions.

Abstract

Vendor interoperability is one of the desired future characteristics of optical networks. This means that the transmission system needs to support a variety of hardware with different components, leading to system uncertainties throughout the network. For example, uncertainties in signal-to-noise ratio and laser linewidth can negatively affect the quality of transmission within an optical network due to e.g. mis-parametrization of the transceiver signal processing algorithms. In this paper, we propose to geometrically optimize a constellation shape that is robust to uncertainties in the channel conditions by utilizing end-to-end learning. In the optimization step, the channel model includes additive noise and residual phase noise. In the testing step, the channel model consists of laser phase noise, additive noise and blind phase search as the carrier phase recovery algorithm. Two noise models are considered for the additive noise: white Gaussian noise and nonlinear interference noise model for fiber nonlinearities. The latter models the behavior of an optical fiber channel more accurately because it considers the nonlinear effects of the optical fiber. For this model, the uncertainty in the signal-to-noise ratio can be divided between amplifier noise figures and launch power variations. For both noise models, our results indicate that the learned constellations are more robust to uncertainties in channel conditions compared to a standard constellation scheme such as quadrature amplitude modulation and standard geometric constellation shaping techniques.