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Rolling Shutter-Based Underwater Optical Camera Communication (UWOCC) with Side Glow Optical Fiber (SGOF)
- Source :
- Applied Sciences, Vol 14, Iss 17, p 7840 (2024)
- Publication Year :
- 2024
- Publisher :
- MDPI AG, 2024.
-
Abstract
- Nowadays, a variety of underwater activities, such as underwater surveillance, marine monitoring, etc., are becoming crucial worldwide. Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted for underwater exploration. Underwater communication via radio frequency (RF) or acoustic wave suffers high transmission loss and limited bandwidth. In this work, we present and demonstrate a rolling shutter (RS)-based underwater optical camera communication (UWOCC) system utilizing a long short-term memory neural network (LSTM-NN) with side glow optical fiber (SGOF). SGOF is made of poly-methyl methacrylate (PMMA) SGOF. It is lightweight and flexibly bendable. Most importantly, SGOF is water resistant; hence, it can be installed in an underwater environment to provide 360° “omni-directional” uniform radial light emission around its circumference. This large FOV can fascinate the optical detection in underwater turbulent environments. The proposed LSTM-NN has the time-memorizing characteristics to enhance UWOCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPB-NN in the UWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-forward error correction (FEC) condition (i.e., bit error rate, BER ≤ 3.8 × 10−3). We also found that thin fiber also allows performing spatial multiplexing to enhance transmission capacity.
- Subjects :
- visible light communication (VLC)
optical wireless communication (OWC)
side glow optical fiber (SGOF)
machine learning
long short-term memory neural network (LSTM-NN)
Technology
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 14
- Issue :
- 17
- Database :
- Directory of Open Access Journals
- Journal :
- Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1e7a05e09353493ca7e1bd5626612086
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/app14177840