A Novel Underwater Wireless Optical Communication Optical Receiver Decision Unit Strategy Based on a Convolutional Neural Network

Intesar F. El Ramley (), Nada M. Bedaiwi, Yas Al-Hadeethi, Abeer Z. Barasheed, Saleha Al-Zhrani and Mingguang Chen
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Intesar F. El Ramley: Physics Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Nada M. Bedaiwi: Physics Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Yas Al-Hadeethi: Physics Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Abeer Z. Barasheed: Physics Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Saleha Al-Zhrani: Physics Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Mingguang Chen: Department of Chemical and Environmental Engineering, University of California, Riverside, CA 92521, USA

Mathematics, 2024, vol. 12, issue 18, 1-37

Abstract: Underwater wireless optical communication (UWOC) systems face challenges due to the significant temporal dispersion caused by the combined effects of scattering, absorption, refractive index variations, optical turbulence, and bio-optical properties. This collective impairment leads to signal distortion and degrades the optical receiver’s bit error rate (BER). Optimising the receiver filter and equaliser design is crucial to enhance receiver performance. However, having an optimal design may not be sufficient to ensure that the receiver decision unit can estimate BER quickly and accurately. This study introduces a novel BER estimation strategy based on a Convolutional Neural Network (CNN) to improve the accuracy and speed of BER estimation performed by the decision unit’s computational processor compared to traditional methods. Our new CNN algorithm utilises the eye diagram (ED) image processing technique. Despite the incomplete definition of the UWOC channel impulse response (CIR), the CNN model is trained to address the nonlinearity of seawater channels under varying noise conditions and increase the reliability of a given UWOC system. The results demonstrate that our CNN-based BER estimation strategy accurately predicts the corresponding signal-to-noise ratio (SNR) and enables reliable BER estimation.

Keywords: convolutional neural network (CNN); signal-to-noise ratio (SNR); bit error rate (BER); eye diagram (ED); computational methods; engineering problems; numerical simulations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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