Research on Link Alignment and Signal Detection Technologies for Cross-water Visible Light Communication

YAN Hongqiang; JIANG Ming

doi:10.13756/j.gtxyj.2025.250001

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Research on Link Alignment and Signal Detection Technologies for Cross-water Visible Light Communication

更新时间：2025-08-16

- Research on Link Alignment and Signal Detection Technologies for Cross-water Visible Light Communication
- Study on Optical Communications Issue 4, (2025)
- 作者机构：
  
  中山大学　电子与信息工程学院（微电子学院），广州　510275
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.250001
  CLC： TN929
- Received：01 January 2025，
  
  Revised：12 March 2025，
  
  Published：10 August 2025
- 稿件说明：
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闫红强，江明. 跨水面VLC链路对准与信号检测技术研究[J]. 光通信研究，2025(4): 250001.

Yan H Q, Jiang M. Research on Link Alignment and Signal Detection Technologies for Cross-water Visible Light Communication[J]. Study on Optical Communications, 2025(4): 250001.
闫红强，江明. 跨水面VLC链路对准与信号检测技术研究[J]. 光通信研究，2025(4): 250001. DOI： 10.13756/j.gtxyj.2025.250001.

Yan H Q, Jiang M. Research on Link Alignment and Signal Detection Technologies for Cross-water Visible Light Communication[J]. Study on Optical Communications, 2025(4): 250001. DOI： 10.13756/j.gtxyj.2025.250001.

摘要

【目的】

随着通信网络技术向第六代移动通信技术（6G）快速演进发展，未来构建空天地海一体化的万物互联网成为可能。其中，跨水面可见光通信（VLC）技术已成为支撑上述新型网络的关键使能技术之一。

【方法】

文章针对现有跨介质水对空（W2A）VLC系统研究的不足，基于所构建的扩展W2A（eW2A）VLC信道模型，针对无人机（UAV）与自主水下载具（AUV）之间通信的W2A-VLC应用场景，提出了一种基于深度学习（DL）的链路对准（LA）方案，可实现收发机通信链路之间的对准。在完成光链路对准的基础上，文章进一步针对未知风速的信道环境设计了一种基于风速估计和信道分类的信号检测方案，可以有效提高W2A-VLC系统在不同水体及风速条件下的信号检测性能。

【结果】

计算结果显示，基于Lognormal和Gamma分布的拟合eW2A信道模型与蒙特卡洛信道仿真的数据匹配度较高，可以较好地模拟跨水面VLC信道的建模。仿真结果表明，在高信噪比区间及不同风速条件下，UAV位于2~30°之间4个角度层的角度估计准确率均可达到100%，具有较好的收敛性，可有效支持DL-LA方案实现UAV移动路径决策。此外，在1~10 m/s的风速区间内，文章所提基于风速估计（WE）和信道分类（CC）的符号检测（SD）方案，即WE-CC-SD方案，可在未经训练的未知风速条件下获得较高的WE准确率及接近理想信道状态条件下的比特误码率（BER）性能。

【结论】

上述结果证明了文章所提DL-LA和WE-CC-SD两种解决方案相比传统方案的优越性，该研究成果可为跨水面VLC技术的完善与发展提供有益参考。

Abstract

【Objective】

As communication network technologies rapidly evolve towards the 6th Generation Mobile Communication Technology (6G)

the future may see the emergence of an integrated internet of everything that covers space

sky

land

and sea scenarios. In this context

cross-water Visible Light Communication (VLC) has emerged as one of the key enabling technologies to support such a new network.

【Methods】

In this paper

we address the shortcomings of existing Water-to-Air (W2A) VLC systems with a Deep Learning (DL) aided Link Alignment (LA) scheme for a W2A-VLC system communication between Unmanned Aerial Vehicle (UAV) and Autonomous Underwater Vehicles (AUV)

based on the proposed extended W2A (eW2A) VLC channel model. Upon the aligned optical link between the transmitter and receiver

we further design a signal detection scheme based on a wind speed estimator and a channel classifier operating in a channel with unknown wind speeds.

【Results】

The results show that the fitted eW2A channel model based on Lognormal and Gamma distributions matches well with the channel data generated by Monte Carlo simulations

accurately modeling the cross-water VLC channel. Simulation results show that under high Signal-to-Noise Ratio (SNR) conditions and different wind speeds

the angle estimation accuracy of the four angular layers located between 2~30° for the UAV can reach 100%. The results exhibit good convergence and can effectively support the UAV path decision-making in the DL-LA scheme. Additionally

in the wind speed range of 1~10 m/s

the Wind-speed Estimation (WE) and Channel Classification (CC) aided Symbol Detection (SD)

namely the WE-CC-SD scheme

can work under untrained unknown wind speeds. This scheme can achieve a high accuracy of WE and a Bit Error Rate (BER) performance close to that achieved under ideal channel state conditions.

【Conclusion】

The above results validate the superiority of the proposed DL-LA and WE-CC-SD schemes over traditional methods. The findings can provide valuable references for the improvement and development of cross-water VLC technologies.

关键词

Keywords

references

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