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Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey

arXiv 2609.22294 ↗First posted 14 Sep 2026eess.SP cs.LGcited by 0

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Abstract (arXiv)

Underwater Optical Wireless Communication (UOWC) has emerged as a promising technology for high-speed underwater data transmission, offering significantly higher bandwidth and lower latency compared to acoustic and Radio Frequency (RF) technologies. However, the underwater medium introduces severe impairments that degrade link performance and limit communication range. The growing complexity of these challenges has increased research interest in Machine Learning (ML) and Deep Learning (DL) approaches, which offer powerful tools for channel modeling, signal processing, and system adaptation in ways that conventional analytical methods struggle to achieve. This survey provides a comprehensive review of ML methods applied across the full UOWC system pipeline, covering channel modeling, transmitter design, receiver detection and equalization, link alignment, and emerging applications, including wireless power transfer, semantic communication, object detection, optical sensing, and localization. Finally, we discuss open challenges and future research directions to motivate further work in this rapidly growing field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 147 references that are in the map. First posted on arXiv 14 Sep 2026.

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23 of 147 references.

References · 23

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11 more
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