Channel-noise tracking for sub-shot-noise-limited receivers with neural networks

TL;DR

This paper demonstrates that neural networks can effectively estimate and compensate for dynamic noise in non-Gaussian optical communication receivers, maintaining quantum-noise-limited performance across various noise conditions.

Contribution

It introduces a neural network-based noise tracking method that enables real-time noise estimation and correction for non-Gaussian receivers in optical communication.

Findings

Neural networks accurately estimate channel noise in simulations.

The method maintains sub-QNL performance across different noise levels.

Noise tracking improves receiver robustness in noisy channels.

Abstract

Non-Gaussian receivers for optical communication with coherent states can achieve measurement sensitivities beyond the limits of conventional detection, given by the quantum-noise limit (QNL). However, the amount of information that can be reliably transmitted substantially degrades if there is noise in the communication channel, unless the receiver is able to efficiently compensate for such noise. Here, we investigate the use of a deep neural network as a computationally efficient estimator of phase and amplitude channel noise to enable a reliable method for noise tracking for non-Gaussian receivers. The neural network uses the data collected by the non-Gaussian receiver to estimate and correct for dynamic channel noise in real-time. Using numerical simulations, we find that this noise tracking method allows the non-Gaussian receiver to maintain its benefit over the QNL across a broad range of strengths and bandwidths of phase and intensity noise. The noise tracking method based on neural networks can further include other types of noise to ensure sub-QNL performance in channels with many sources of noise.