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Beam wander prediction with recurrent neural networks.

Dmitrii Briantcev, Mitchell A Cox, Abderrahmen Trichili

|September 15, 2023

Summary

This study introduces a recurrent neural network (RNN) to predict beam wander in free-space optical communication (FSO). The RNN method significantly improves prediction accuracy, outperforming traditional approaches for stable optical links.

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Area of Science:

Optical Engineering
Artificial Intelligence
Telecommunications

Background:

Beam wander is a major challenge for free-space optical communication (FSO) systems.
Misalignment of structured light beams in FSO systems leads to increased receiver crosstalk.
Turbulence-induced fading impacts the reliability of FSO links.

Purpose of the Study:

To develop and evaluate a recurrent neural network (RNN) for predicting beam wander in FSO systems.
To investigate the performance of the RNN approach across different beam types and data sampling scenarios.
To assess the potential of RNN-based prediction for mitigating FSO link impairments.

Main Methods:

A recurrent neural network (RNN) model was employed to predict future beam wander based on historical beam center of mass positions.
The RNN approach was tested using both under-sampled experimental data (260 m link) and over-sampled simulated data.
The study analyzed beam wander for Gaussian, Hermite-Gaussian, and Laguerre-Gaussian beams.

Main Results:

The RNN-based prediction method demonstrated a 20-40% improvement in prediction error compared to naive and linear methods.
The approach showed strong performance across various scenarios, including predicting multiple samples ahead.
The RNN model matched or exceeded the performance of other prediction methods in all investigated cases.

Conclusions:

The proposed RNN solution effectively predicts beam wander in FSO systems, outperforming existing methods.
This predictive capability can help mitigate turbulence-induced fading and enhance FSO system reliability.
Potential applications include intelligent re-transmits, quality of service improvements, and predictive adaptive optics.