光通信技术2026，Vol.50Issue(3)：1-11,11.DOI:10.13921/j.cnki.issn1002-5561.2026.03.001
深度学习赋能的水下无线光通信研究进展
Advances in deep learning-enabled underwater wireless optical communication
摘要
Abstract
Underwater wireless optical communication(UWOC)offers advantages such as wide bandwidth,high data transmis-sion rates,and low-cost,compact transmitters,making it a key supporting technology in the field of marine communication.However,the underwater channel environment is complex and affected by various factors including absorption,scattering,and turbulence,which limit the performance of traditional methods in complex dynamic scenarios.In recent years,deep learning(DL),with its powerful nonlinear fitting capabilities and data-driven advantages,has provided important support for the devel-opment of UWOC.This paper summarizes the application of DL in key technical areas of UWOC,including channel estima-tion,signal detection,modulation format recognition,orbital angular momentum(OAM)mode recognition,and network optimi-zation.Finally,the future application of DL in UWOC is summarized and prospected.关键词
水下无线光通信/深度学习/信道估计/信号检测/调制格式识别/轨道角动量模态识别/组网优化Key words
underwater wireless optical communication/deep learning/channel estimation/signal detection/modulation for-mat recognition/modal identification of orbital angular momentum/networking optimization分类
信息技术与安全科学引用本文复制引用
朱宏娜,刘仲涛,朱宇轩,于水,郎子乂,罗斌,吴宗玲..深度学习赋能的水下无线光通信研究进展[J].光通信技术,2026,50(3):1-11,11.基金项目
国家重点研发计划项目(2021YFC3101402)资助 （2021YFC3101402）
中央高校基本科研业务费专项(2682025XJ020)资助. （2682025XJ020）
