2022
DOI: 10.1364/oe.449393
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2D least-squares mode decomposition for mode division multiplexing

Abstract: We investigate a fast and accurate technique for mode decomposition in multimode optical fibers. Initial decomposition task of near-field beam patterns is reformulated in terms of a system of linear equations, requires neither machine learning nor iterative routines. We apply the method to step and graded-index fibers and compare the decomposition performance. We determine corresponding application boundaries, propose an efficient algorithm for phase retrieval and carry out a specific preselective procedure th… Show more

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Cited by 7 publications
(3 citation statements)
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“…However, random receiver noise often plays a crucial role in MD. There is always a trade-off between noise levels and the number of decomposable modes [30,31] . Therefore, investigation of the noise influence on MM-DBP performance is of paramount importance.…”
Section: Resultsmentioning
confidence: 99%
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“…However, random receiver noise often plays a crucial role in MD. There is always a trade-off between noise levels and the number of decomposable modes [30,31] . Therefore, investigation of the noise influence on MM-DBP performance is of paramount importance.…”
Section: Resultsmentioning
confidence: 99%
“…In order to train the MM-DBP network, we need pairs of input and output beam speckles that require numerical simulation of multimode signal propagation. Details on the MD-DNN architecture lie beyond the scope of the current work, as this part is purely technical and can be given by any other method [30,31] . In what follows, under MD-DNN, we understand an appropriately trained vision transformer [33] .…”
Section: The Main Ideamentioning
confidence: 99%
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