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Updated: May 5, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Exploiting multimode fiber speckle for turbulence-resilient OAM classification.

Christopher R Rawlings, Mitchell A Cox

|May 4, 2026

Summary

This summary is machine-generated.

A novel passive fiber optic reservoir system enhances free-space optical communication by providing turbulence-resilient orbital angular momentum (OAM) classification. This method outperforms traditional techniques in atmospheric turbulence, enabling robust OAM recovery.

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Last Updated: May 5, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Published on: November 7, 2016

Area of Science:

Optical Communications
Wavefront Engineering
Machine Learning

Background:

Free-space optical communication utilizes orbital angular momentum (OAM) for high channel capacity.
Atmospheric turbulence degrades the modal orthogonality essential for conventional OAM receivers.
Existing methods struggle with maintaining signal integrity under turbulent atmospheric conditions.

Purpose of the Study:

To develop a turbulence-resilient method for orbital angular momentum (OAM) classification in free-space optical communication.
To demonstrate the efficacy of a passive multimode fiber reservoir acting as an optical extreme learning machine.
To outperform traditional modal decomposition techniques in moderate-to-strong atmospheric turbulence.

Main Methods:

Employing a passive multimode fiber reservoir as an optical extreme learning machine.
Mapping distorted OAM wavefronts into a high-dimensional speckle feature space.
Utilizing linear readouts for OAM classification after wavefront distortion.

Main Results:

Achieved high accuracy in classifying structural (MNIST, 97.6%) and textural (Fashion-MNIST, 87.1%) benchmarks.
Demonstrated superior performance over ideal modal decomposition in moderate-to-strong turbulence (exceeding 20% in strong turbulence regimes, SR ≤ 0.5).
Showcased the system's ability to maintain OAM class separability in a high-dimensional feature space.

Conclusions:

Passive optical scattering in a fiber reservoir effectively processes complex wavefronts for robust OAM recovery.
The optical extreme learning machine approach offers a significant advancement for free-space optical communication under atmospheric turbulence.
This all-optical method enables simple linear readouts for reliable OAM classification, even when modal orthogonality is compromised.