Wave-Distorted Signal Correction Using a Convolutional Neural Network in a Water-Air OWC System

A convolutional neural network-based detector is designed to mitigate wave-induced power fluctuation in the wavy WA-OWC channel. The packet loss rate was significantly reduced from 38 % to 5 %, at a data rate of 1 Gbit/s.

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Publication Details

Published
2025-06-29
DOI
https://doi.org/10.23919/oecc/psc62146.2025.11111509
Primary Topic
Underwater Vehicles and Communication Systems
Type
article
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article

Wave-Distorted Signal Correction Using a Convolutional Neural Network in a Water-Air OWC System

Anzi Xu, Yujie Di, Lian-Kuan Chen
Underwater Vehicles and Communication Systems
article

Wave-Distorted Signal Correction Using a Convolutional Neural Network in a Water-Air OWC System

Anzi Xu, Yujie Di, Lian-Kuan Chen
article en

Abstract

A convolutional neural network-based detector is designed to mitigate wave-induced power fluctuation in the wavy WA-OWC channel. The packet loss rate was significantly reduced from 38 % to 5 %, at a data rate of 1 Gbit/s.

Chinese University of Hong Kong (HK)
Clean water and sanitation
Openalex Percentile: Top 74%
Underwater Vehicles and Communication Systems
DOISourceOpenAlex
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Wave-Distorted Signal Correction Using a Convolutional Neural Network in a Water-Air OWC System — Anzi Xu, Yujie Di, et al. · (2025) | TGRS Research Map | TGRS