Source data encoding and beam generation in underwater optical communication using Laguerre-Gaussian modes

Underwater wireless optical communication (UWOC) is a promising solution for high-speed data transfer in marine environments. However, underwater turbulence and scattering significantly degrade signal integrity. The objective of this study is to enhance the reliability and efficiency of underwater communication systems by improving signal decoding accuracy in the presence of distortion. In this paper, a novel approach to source data encoding and beam generation in underwater optical communication using Laguerre-Gaussian (LG) modes is proposed. Initially, the conversion of source data into digital code is followed by mode matching, which encodes data into specific phase modes. A Bessel-Gaussian laser beam is generated to carry the encoded information, which is then transformed into LG beams using a spatial light modulator (SLM). These beams, characterised by their orbital angular momentum (OAM) properties, propagate through various underwater environments, including pure seawater, coastal seawater, and turbid water, which introduce different levels of distortion. Distorted LG beams are captured by an underwater camera and processed by a computer. A deep learning model, Res-GoogleNet, is employed to accurately recognize the mode and decode the distorted OAM patterns. Finally, the decoded mode information is used to reconstruct the original data, ensuring efficient and reliable underwater communication. The efficacy of the proposed technique is assessed using Matlab-2019b, the underwater optical communication system is discovered on a Windows OS using 16 GB of RAM and an Intel Core i7 CPU. The proposed model improves accuracy, evaluation metrics of accuracy, precision, recall, signal-to-noise ratio (SNR) and bit error rate (BER). The proposed model achieves a high accuracy rate of 99.07%, surpassing the efficiency of existing approaches. The proposed model improves its accuracy by 5.47%, 1.80%, 0.62%, and 2.97% compared to a diffractive deep neural network (DDNN), a multichannel neural network (MCNN), and a deep convolutional neural network radio frequency (DCNN-RF), respectively.