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arXiv 2409.03250eess.SP

面向水-空直接光无线通信的强化学习使能波束对准

Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications

Jiayue Liu, Tianqi Mao, Dongxuan He, Yang Yang, Zhen Gao, Dezhi Zheng, Jun Zhang

AI总结:

本文针对高海况下水-空直接光无线通信的波束偏差问题,提出基于深度强化学习(DDPG)的波束对准策略,结合海浪谱建模、光线追踪信道表征和对数-指数非线性奖励函数,仿真验证了其优越性。

摘要:

水下探测/侦察应用日益增长的兴趣推动了从水下到空中中继平台的高速率数据传输需求,尤其是在高海况场景下。得益于其宽带宽和优异的保密性,光无线通信已成为水-空传输的一种有前景的候选方案。然而,在缺乏中继船舶/浮标的情况下,光信号在穿越高度动态的水-空界面时不可避免地会产生偏差。为解决这一问题,本文提出了一种基于深度强化学习(DRL)的新型波束对准策略,用于水-空直接光无线通信。具体而言,首先利用海浪谱分析对动态水-空界面进行数学建模,随后采用光线追踪技术对传播信道进行表征。然后引入深度确定性策略梯度(DDPG)方案,用于基于DRL的收发波束对准。设计了一种关于接收信号强度的对数-指数(LE)非线性奖励函数,以实现不同动作之间的高分辨率奖励。仿真结果验证了所提出的基于DRL的波束对准方案的优越性。

英文摘要:

The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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