aiqbench analysis
This survey consolidates how machine learning addresses severe channel impairments in underwater optical wireless communication, covering the full pipeline from modeling to semantic applications. By replacing conventional analytical methods with deep learning for signal processing and system adaptation, it highlights a critical intersection where AI enables high-bandwidth data transmission in hostile environments. The work underscores ML's necessity for overcoming physical limitations that traditional engineering approaches cannot resolve alone.
Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey
arXiv:2609.22294v1 Announce Type: cross Abstract: 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.
Abstract: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.
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2609.22294 [eess.SP]
(or arXiv:2609.22294v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2609.22294 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 243 words
