Underwater optical wireless communication system performance improvement using convolutional neural networks

Abstract

Many applications that could benefit from the underwater optical wireless communication technique face challenges in using this technology due to the substantial, varying attenuation that affects optical signal transmission through waterbodies. This research demonstrated that convolutional neural networks (CNNs) could readily address these problems. A modified CNN model was proposed to recover the original data of a non-return to zero on–off keying modulated signal transmitted optically through a tank full of Gulf seawater. A comparison between the proposed CNN model and a conventional fixed-threshold decoder (FTD) demonstrates the excellent performance of the proposed CNN model, which improved the bit error ratio (BER), signal-to-noise ratio (SNR), and effective channel length. The BER of the optical signals that are transmitted at powers of 24, 26, and 27 dBm and a bit rate of 10 Mbit/s at a distance of 3 m from the transmitter when FTD is used is 7.826 × 10−7, 5.049 × 10−8, and 8.38 × 10−10, respectively. When the CNN decoder is used at the same distance and powers, the BER is 6.23 × 10−14, 1.44 × 10−16, and 2.69 × 10−18, respectively. In conclusion, the BER decreased by about seven orders of magnitude, the effective channel length increased by four times, and the SNR decreased by about 20 dB. The simplicity of the proposed CNN decoder is independent of the prior knowledge of the channel conditions. Furthermore, the magnificent obtained results make the proposed CNN decoder an ideal substitute for ordinary underwater optical wireless communication decoders.

A 930 m/180 Gbps*User Underwater Coherent Optical Code-Division Multiple-Access Network Based on Hybrid 256-Differential Pulse Position Modulation and Weighted Modified Prime Code Sequence

“…Various UOWC system models are used to represent the effect of turbulence on the system performance; however, for weak turbulence, the gamma-gamma model is suitable. The probability density function of the irradiance I r investigates the effect of turbulence on system performance, and various UOWC channel models can be expressed as in [11] as follows:…”

“…Numerous studies have been conducted to enhance the performance of acoustic communication channels [3][4][5][6][7]. Nonetheless, its performance is related to its physical nature, which limits the bandwidth, results in high potential, and produces high transmission losses, time-varying multi-path propagation, and Doppler spread [8][9][10][11][12][13]. These limitations prevent autonomous underwater vehicles from transmitting high-definition, real-time videos via acoustic communication.…”

A Salinity-Impact Analysis of Polarization Division Multiplexing-Based Underwater Optical Wireless Communication System with High-Speed Data Transmission

“…Simulation results show significant BER reduction in the ACPS-based UWOC-NOMA system compared to conventional systems, considering light-intensity scintillation. Another study in 2023 [19] demonstrates the effectiveness of a modified CNN model in addressing attenuation challenges in underwater optical wireless communication. The proposed CNN decoder significantly improves BER, SNR, and effective channel length compared to a conventional decoder.…”