{"title":"基于强化学习的水下无线光通信自适应波束转向与发散控制","authors":"Takumi Ishida;Chedlia Ben Naila;Hiraku Okada","doi":"10.1109/JPHOT.2025.3569824","DOIUrl":null,"url":null,"abstract":"Underwater optical wireless communication (UOWC) is a promising technology enabling high-speed, low-latency communication for beyond 5G/6G systems. However, UOWC faces significant challenges due to the complex nature of the underwater channel, including absorption, scattering, turbulence, and dynamic sea wave conditions, which complicate static analysis. To address these challenges, we propose a neural network-based beam adaptation technique for UOWC systems, combining deep Q-networks (DQN) and long short-term memory (LSTM) models. These models dynamically optimize beam divergence and steering angles based on the properties of sea waves. Our approach offers a robust solution to maintaining communication quality in diverse and challenging underwater environments. In this work, the turbulence is modeled using the exponential generalized gamma (EGG) distribution, which provides an excellent fit for various types of turbulence. Simulation results show that the proposed LSTM-DQN-based approach consistently outperforms fixed-beam, DQN-only, and heuristic methods in a range of underwater environments. The system successfully compensates for random vessel movements and turbulence-induced intensity fluctuations, ensuring reliable communication. These results highlight the effectiveness of the LSTM-DQN-based method in optimizing beam alignment under various water conditions. Furthermore, a comparison with other machine learning (ML) methods revealed that similar performance can be achieved with those techniques. However, the proposed method demonstrated superior stability. By accounting for variations in vessel size and the movement of the transmitter, we have shown that the proposed method is effective under different environmental conditions.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 3","pages":"1-10"},"PeriodicalIF":2.1000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11003393","citationCount":"0","resultStr":"{\"title\":\"Adaptive Beam Steering and Divergence Control for Underwater Optical Wireless Communication Using Reinforcement Learning\",\"authors\":\"Takumi Ishida;Chedlia Ben Naila;Hiraku Okada\",\"doi\":\"10.1109/JPHOT.2025.3569824\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Underwater optical wireless communication (UOWC) is a promising technology enabling high-speed, low-latency communication for beyond 5G/6G systems. However, UOWC faces significant challenges due to the complex nature of the underwater channel, including absorption, scattering, turbulence, and dynamic sea wave conditions, which complicate static analysis. To address these challenges, we propose a neural network-based beam adaptation technique for UOWC systems, combining deep Q-networks (DQN) and long short-term memory (LSTM) models. These models dynamically optimize beam divergence and steering angles based on the properties of sea waves. Our approach offers a robust solution to maintaining communication quality in diverse and challenging underwater environments. In this work, the turbulence is modeled using the exponential generalized gamma (EGG) distribution, which provides an excellent fit for various types of turbulence. Simulation results show that the proposed LSTM-DQN-based approach consistently outperforms fixed-beam, DQN-only, and heuristic methods in a range of underwater environments. The system successfully compensates for random vessel movements and turbulence-induced intensity fluctuations, ensuring reliable communication. These results highlight the effectiveness of the LSTM-DQN-based method in optimizing beam alignment under various water conditions. Furthermore, a comparison with other machine learning (ML) methods revealed that similar performance can be achieved with those techniques. However, the proposed method demonstrated superior stability. By accounting for variations in vessel size and the movement of the transmitter, we have shown that the proposed method is effective under different environmental conditions.\",\"PeriodicalId\":13204,\"journal\":{\"name\":\"IEEE Photonics Journal\",\"volume\":\"17 3\",\"pages\":\"1-10\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11003393\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Photonics Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11003393/\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Journal","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11003393/","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Adaptive Beam Steering and Divergence Control for Underwater Optical Wireless Communication Using Reinforcement Learning
Underwater optical wireless communication (UOWC) is a promising technology enabling high-speed, low-latency communication for beyond 5G/6G systems. However, UOWC faces significant challenges due to the complex nature of the underwater channel, including absorption, scattering, turbulence, and dynamic sea wave conditions, which complicate static analysis. To address these challenges, we propose a neural network-based beam adaptation technique for UOWC systems, combining deep Q-networks (DQN) and long short-term memory (LSTM) models. These models dynamically optimize beam divergence and steering angles based on the properties of sea waves. Our approach offers a robust solution to maintaining communication quality in diverse and challenging underwater environments. In this work, the turbulence is modeled using the exponential generalized gamma (EGG) distribution, which provides an excellent fit for various types of turbulence. Simulation results show that the proposed LSTM-DQN-based approach consistently outperforms fixed-beam, DQN-only, and heuristic methods in a range of underwater environments. The system successfully compensates for random vessel movements and turbulence-induced intensity fluctuations, ensuring reliable communication. These results highlight the effectiveness of the LSTM-DQN-based method in optimizing beam alignment under various water conditions. Furthermore, a comparison with other machine learning (ML) methods revealed that similar performance can be achieved with those techniques. However, the proposed method demonstrated superior stability. By accounting for variations in vessel size and the movement of the transmitter, we have shown that the proposed method is effective under different environmental conditions.
期刊介绍:
Breakthroughs in the generation of light and in its control and utilization have given rise to the field of Photonics, a rapidly expanding area of science and technology with major technological and economic impact. Photonics integrates quantum electronics and optics to accelerate progress in the generation of novel photon sources and in their utilization in emerging applications at the micro and nano scales spanning from the far-infrared/THz to the x-ray region of the electromagnetic spectrum. IEEE Photonics Journal is an online-only journal dedicated to the rapid disclosure of top-quality peer-reviewed research at the forefront of all areas of photonics. Contributions addressing issues ranging from fundamental understanding to emerging technologies and applications are within the scope of the Journal. The Journal includes topics in: Photon sources from far infrared to X-rays, Photonics materials and engineered photonic structures, Integrated optics and optoelectronic, Ultrafast, attosecond, high field and short wavelength photonics, Biophotonics, including DNA photonics, Nanophotonics, Magnetophotonics, Fundamentals of light propagation and interaction; nonlinear effects, Optical data storage, Fiber optics and optical communications devices, systems, and technologies, Micro Opto Electro Mechanical Systems (MOEMS), Microwave photonics, Optical Sensors.