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Design of An Autoencoder-based Underwater Optical Communication Transceiver in Attenuation Channel

Published: 31 May 2020 Publication History

Abstract

In this paper, an effective communication system is designed for an underwater optical communication system, combined with an autoencoder (AE) in deep learning, using the AWGN channel, and considering the attenuation caused by underwater scattering and absorption. I have considered two system models, space division multiplexing and single channel, and performed multiple simulations for different communication rates. Under the best convergence condition of AE, by comparing the error performance between the AE and the conventional modulation and demodulation method, it can be seen that the performance of the AE is better than the conventional model method. By drawing the constellation diagram learned by the AE, it can be seen that the AE has better symbol quality in the case of large symbol complexity, and it can be proved that the AE can achieve the best performance of the conventional model method.

References

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    CNIOT '20: Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things
    April 2020
    234 pages
    ISBN:9781450377713
    DOI:10.1145/3398329
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • University of Salamanca: University of Salamanca
    • The University of Adelaide, Australia
    • Edinburgh Napier University, UK: Edinburgh Napier University, UK
    • University of Sydney Australia

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    New York, NY, United States

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    Published: 31 May 2020

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    Author Tags

    1. Autoencoder
    2. Deep learning
    3. Single channel
    4. Space division multiplexing
    5. Underwater optical communication

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