Channel optimization in mode division multiplexing using neural networks

Mode division multiplexing (MDM) has emerged as a new multiplexing paradigm for enhancing the bandwidth by leveraging the orthogonal modes as a parallel channel for transferring information. Although capacity gains theoretically increase in relation to the number of modes in MDM, mode coupling inevi...

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Bibliographic Details
Published in2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) pp. 173 - 175
Main Authors Fazea, Yousef, Sajat, Mohd Samsu, Ahmad, Amran, Alobaedy, Mustafa Muwafak
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Channel estimation
Channel modeling
Deep learning
Equalizers
Feed-forward back propagation algorithm
Multiplexing
Neurons
Optical fiber dispersion
Optical fiber networks
Optical fibers
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Summary:Mode division multiplexing (MDM) has emerged as a new multiplexing paradigm for enhancing the bandwidth by leveraging the orthogonal modes as a parallel channel for transferring information. Although capacity gains theoretically increase in relation to the number of modes in MDM, mode coupling inevitably causes modes to interchange power randomly, leading to channel degradation from different arrival mode delay and inter-symbol interference (ISI). Hence, this paper demonstrates a new neural network feed-forward and back propagation equalizer to mitigate pulse broadening caused by mode-coupling.
DOI:10.1109/CSPA.2018.8368707