Makoni, Steven Gamuchirai2025-07-142024Makoni, Steven Gamuchirai. (2024). Modelling OAM Crosstalk with Neural Networks: Impact of Tip/tilt and Lateral Displacement [Masters dissertation, University of the Witwatersrand, Johannesburg]. WIReDSpace.https://hdl.handle.net/10539/45428A research report submitted in fulfillment of the requirements for the Master of Science in Engineering, In the Faculty of Engineering and the Built Environment , School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, 2024This research focuses on a critical challenge within Free Space Optical ( FSO) commu- nication systems, specifically those utilizing Mode Division Multiplexing (MDM) with Orbital Angular Momentum ( OAM ) modes of a limited transmission range. Despite these systems’ potential to significantly enhance spectral efficiency and transmission capacity, their effectiveness is hindered by the limited range caused by atmospheric turbulence-induced aberrations. Atmospheric turbulence and mis- alignments distort the optical wavefront, causing degradation in orthogonal spatial modes and resulting in power spreading into adjacent modes, known as crosstalk in MDM systems. This research presents a simple neural network model for estimating OAM crosstalk in FSO systems, specifically focusing on atmospheric turbulence-induced aberrations. Firstly, we generated datasets through simulation and experimentation for validation purposes. We then develop and evaluate the neural network model, assessing its accuracy under various turbulence aberrations. The simple neural network, trained solely on tip/tilt and displacement inputs and without retraining, accurately estimated OAM spectra using approximated inputs in turbulent condi- tions, closely matching experimentally measured spectra. Despite the presence of turbulent aberrations, the model showed a minimal decrease in the coefficient of determination, indicating its ability to generalize well to unseen measurements. Our findings indicate that a simple neural network trained solely on tilt and displacement inputs can accurately estimate OAM crosstalk amidst many turbulence aberrations for ℓ ∈ [-5, 5] as a proof of concept. This implies that simple detectors such as cameras can be used to implement or optimize digital signal processing for error detection and correction utilizing the knowledge of crosstalk, offering promising avenues for improving system efficiency and quality of service for MDM systems. In summary, this research leveraged neural networks to model OAM crosstalk induced by misalignments and turbulence. The model’s ability to estimate OAM crosstalk due to misalignments and atmospheric turbulence shows potential for use in real-time predictive systems. With further refinement, neural network models could indicate the evolution of OAM crosstalk in FSO communications that em- ploy OAM multiplexing schemes in atmospheric turbulence. The demonstrated efficacy of the neural network model positions it as a valuable tool for enhancing the robustness of FSO communications employing higher-order OAM modes.en© 2024 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.UCTDFree Space OpticalMode Division MultiplexingOrbital Angular Momentum beamsTurbulenceOAM crosstalkMisalignmentsModal DecompositionNeural networksDissertationUniversity of the Witwatersrand, JohannesburgSDG-8: Decent work and economic growth