Reliable Deep-Learning based Underwater Optical OFDM Wireless Communications

Zeyad A. H. Qasem; Amjad Ali; Bohua Deng; Qian Li; H. Y. Fu

doi:10.1109/oecc56963.2023.10209946

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JOURNAL ARTICLE

Reliable Deep-Learning based Underwater Optical OFDM Wireless Communications

Zeyad A. H. Qasem Amjad Ali Bohua Deng Qian Li H. Y. Fu

Year: 2023 Pages: 1-4

DOI: 10.1109/oecc56963.2023.10209946

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Abstract

We propose and demonstrate deep learning-based fast-orthogonal frequency division multiplexing (FOFDM-DL) system for underwater optical wireless communications. FOFDM-DL significantly outperforms the conventional OFDM in terms of peak-to-average power ratio (PAPR), and bit error rate (BER).

Keywords:

Orthogonal frequency-division multiplexing Bit error rate Computer science Wireless Optical wireless Underwater Underwater acoustic communication Electronic engineering Telecommunications Engineering Channel (broadcasting) Geology

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Topics

Optical Wireless Communication Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Underwater Vehicles and Communication Systems

Physical Sciences → Engineering → Ocean Engineering

PAPR reduction in OFDM

Physical Sciences → Engineering → Electrical and Electronic Engineering

Reliable Deep-Learning based Underwater Optical OFDM Wireless Communications

Abstract

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Citation History

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