Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

Mohammed Salim, Omar N.; Adnan, Salah A.; Mutlag, Ammar H.

doi:10.30684/etj.2022.136247.1309

	Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System
Engineering and Technology Journal
Article 16, Volume 41, Issue 2, 2023, Pages 456-466 PDF (1.19 M)
Document Type: Research Paper
DOI: 10.30684/etj.2022.136247.1309
Authors
Omar N. Mohammed Salim¹; Salah A. Adnan^* ²; Ammar H. Mutlag¹
¹Middle Technical University
²Laser and optoelectronics engineering, university of technology Iraq Baghdad
Abstract
Several scientists have proposed using an underwater wireless optical communication system (UWOC) to deliver high-speed data services using the abundant optical spectrum. However, wireless optical signal propagation faces an antagonistic environment when using undersea channels due to various factors like scattering, absorption, turbulence, and optical link misalignment between the transmitter and the receiver. These factors will attenuate the optical signal and lead to degrading system performance. To reduce these factors impact on the communication system's performance, transmitted optical power (OTP) should be increased. Since the UWOC system is battery-powered, increasing OTP will consume more electrical power. Therefore, it is necessary to adjust OTP to a value commensurate with the underwater channel changes. So, an ANN model is proposed in this article for link adaptation, which can adjust the OTP level in tandem with the underwater channel conditions. Data for training, testing, and validation of the proposed system reliability was collected experimentally, and tap water was used as a transmission medium. Evaluation of the proposed model outcome demonstrates that reliable performance is achieved in predicting OTP needed in multiple scenarios. The MSE of the predicted OTP is(9.5×10-3,1.5×10-2, and 1.7×10-2) dBm in the training, testing, and validation stages, respectively. The regression values of the training, testing and validation sets are (0.9997,0.9990, and 0.9996). The results achieved by the proposed model prove it is reliable to be applied in UWOC systems.
Highlights
A 450 nm Underwater Wireless Optical Communication System was implemented in this study. Bit error rate was measured in the tap water channel for 2Mbit/s,10Mbit/s, and 20Mbit/s. The optimization for striking a balance between low power consumption and reliable data transfer in underwater ambient was investigated. A FFBP-ANN model-based transmission power control for the UWOC system has been adopted. power needed in multiple scenarios.
Keywords
UWOC; ANN; transmitted power control; neural network; back-propagation algorithm

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