发表机构
Nanonetworking Center in Catalunya, Universitat Politècnica de Catalunya（加泰罗尼亚纳米网络中心，加泰罗尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于深度强化学习的3D波束自适应方法，解决AUV在水下无线光通信中的对准问题，通过联合优化波束转向和发散角，将额外中断率控制在8.5%至16.2%以内。
AI 中文摘要
水下无线光通信（UWOC）为自主水下航行器（AUV）提供必不可少的高数据速率，但收发器之间的对准误差严重影响了连接的可靠性。本文针对在未知洋流作用下移动AUV的波束指向问题，提出了一种基于深度强化学习（DRL）的解决方案。我们开发了一个全面的三维UWOC信道模型，该模型结合了深度相关的衰减、湍流以及离散射线方法，以精确量化几何和对准损耗。所得到的智能体联合优化波束转向和发散角，学习一种优先考虑持续链路维护的策略。利用真实海洋学数据进行评估，通过额外中断度量来评估框架的性能，该度量隔离了仅由指向误差引起的中断。相对于节点之间的完美对准，所提出的方法在无洋流跟踪条件下将该度量限制在8.5%，在受洋流引起的漂移影响时将其限制在16.2%。本文的研究结果证实，基于DRL的波束控制能够在动态水下环境中实现自适应跟踪并减轻链路中断。
英文摘要
Underwater Wireless Optical Communications (UWOC) provide essential high data rates for Autonomous Underwater Vehicles (AUVs), but reliable connectivity is critically affected by transmitter-receiver misalignment. This work addresses the beam pointing problem for a moving AUV subject to unknown ocean currents through a Deep Reinforcement Learning (DRL) framework. We develop a comprehensive 3D UWOC channel model incorporating depth-dependent attenuation, turbulence, and a discrete-ray method to accurately quantify geometric and misalignment losses. The resulting agent jointly optimizes beam steering and divergence angles, learning a policy that prioritizes continuous link maintenance. Evaluated using real oceanographic data, the framework's performance is assessed via the excess outage metric, which isolates outages occurring exclusively due to pointing errors. Relative to perfect alignment between nodes, the proposed method bounds this metric to 8.5% under ocean current-free tracking conditions and limits it to 16.2% when subjected to current-induced drift. The findings obtained in this work confirms that DRL-enabled beam control is capable of adaptive tracking and mitigating link outages in dynamic underwater environments.
Comments6 pages, in IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), workshop on Optical Wireless Communications and Sensing in Sea, Air, and Space (OWC-SAS)
