AI 中文总结
本文全面综述了机器学习在水下无线光通信系统中的应用，涵盖信道建模、收发机设计、链路对准及新兴应用，并指出未来研究方向。
AI 中文摘要
水下无线光通信（UOWC）已成为一种有前景的高速水下数据传输技术，与声学和射频（RF）技术相比，它提供了显著更高的带宽和更低的延迟。然而，水下介质引入了严重的损伤，这些损伤会降低链路性能并限制通信范围。这些挑战日益增长的复杂性增加了对机器学习（ML）和深度学习（DL）方法的研究兴趣，这些方法为信道建模、信号处理和系统自适应提供了强大的工具，其方式传统分析方法难以实现。本综述全面回顾了应用于整个UOWC系统流程的机器学习方法，涵盖信道建模、发射机设计、接收机检测与均衡、链路对准以及新兴应用，包括无线能量传输、语义通信、目标检测、光学传感和定位。最后，我们讨论了开放的挑战和未来的研究方向，以激励这一快速发展领域的进一步工作。
英文摘要
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.
