Underwater VLC channel estimation based on the deep combined Q-network
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Nidhal Abd Mohammed
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Mehdi Chehel Amirani
Abstract
Underwater visible light communication (VLC) systems are increasingly considered as a promising alternative for communication in aquatic environments due to their high data rates and low latency. However, the performance of VLC systems in underwater environments is significantly affected by factors such as distance and salinity level, leading to channel impairments that must be accurately estimated for reliable communication. In this paper, we propose a novel approach for channel estimation in underwater VLC (UVLC) systems, where the channel model is assumed to follow a log-normal distribution based on the distance and salinity level of the water. We leverage the power of deep learning, specifically a deep neural network with Q-layers, to estimate the channel conditions dynamically. The Q-layers enable the network to model the nonlinearities inherent in the channel estimation problem, providing robust performance even in the presence of varying environmental conditions. Our results demonstrate that the proposed method achieves superior performance compared to traditional estimation techniques, offering a significant improvement in the reliability and accuracy of underwater VLC systems. Simulation results show that the proposed Q-network-based estimator achieves up to 31 % lower NMSE than ChanEstNet under high turbulence (distance >15 m), and improves BER performance by nearly 18 % in long-distance UVLC conditions.
Acknowledgments
The authors would like to thank their institutes for their support and assistance during the course of this study.
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Research ethics: This study was conducted in accordance with the ethical standards.
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Informed consent: Not applicable.
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Author contributions: [Author 1 and 2]: Conceptualization, Methodology, Writing – Original Draft, [Author 3]: Data curation, Formal analysis, Writing – Review and Editing, All authors read and approved the final manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: The authors declare that no generative AI or machine learning tools were used for the generation of data, figures, or analysis in this study.
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Conflict of interest: The authors declare that they have no conflict of interest.
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Research funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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