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

arXiv: https://arxiv.org/abs/2609.22294 · HTML: https://mlsanity.com/paper/2609.22294/

## 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.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019)
- [Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning](https://mlsanity.com/paper/1706.05064/) (2017)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks](https://mlsanity.com/paper/2102.03832/) (2021)
- [End-to-end Deep Learning of Optical Fiber Communications](https://mlsanity.com/paper/1804.04097/) (2018)
- [Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention](https://mlsanity.com/paper/1608.06409/) (2016)
- [Physical Layer Communications System Design Over-the-Air Using Adversarial Networks](https://mlsanity.com/paper/1803.03145/) (2018)
- [Underwater Acoustic Communication Channel Modeling using Deep Learning](https://mlsanity.com/paper/2201.10056/) (2022)
- [Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches](https://mlsanity.com/paper/2505.01234/) (2025)
- [A Generalization Theory for Zero-Shot Prediction](https://mlsanity.com/paper/2507.09128/) (2025)
- [Underwater Optical Wireless Communications, Networking, and Localization: A Survey](https://mlsanity.com/paper/1803.02442/) (2018)
- [Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems](https://mlsanity.com/paper/2209.08333/) (2022)
- [Unified Statistical Channel Modeling and performance analysis of Vertical Underwater Wireless Optical Communication Links considering Turbulence-Induced Fading](https://mlsanity.com/paper/2308.05760/) (2023)

## 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.