A Transmission Scheme with Dynamic Learning of Channel Characteristics for Blue-Green Laser Air-Sea Cross-Medium Communication

ZHANG Zijin; LI Zhuo; XING Lijuan

doi:10.12263/DZXB.20260077

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A Transmission Scheme with Dynamic Learning of Channel Characteristics for Blue-Green Laser Air-Sea Cross-Medium Communication

更新时间：2026-06-08

- A Transmission Scheme with Dynamic Learning of Channel Characteristics for Blue-Green Laser Air-Sea Cross-Medium Communication
- ACTA ELECTRONICA SINICA Pages: 1-10(2026)
- 作者机构：
  
  1.西安电子科技大学广州研究院，广东广州 510555
  2.西安电子科技大学通信工程学院，陕西西安 710071
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20260077
  CLC： TN929.1;
- Received：06 March 2026，
  
  Accepted：19 April 2026，
  
  Online First：08 June 2026，
- 稿件说明：
移动端阅览
ZHANG Zijin, LI Zhuo, XING Lijuan. A Transmission Scheme with Dynamic Learning of Channel Characteristics for Blue-Green Laser Air-Sea Cross-Medium Communication[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-10.
DOI：

ZHANG Zijin, LI Zhuo, XING Lijuan. A Transmission Scheme with Dynamic Learning of Channel Characteristics for Blue-Green Laser Air-Sea Cross-Medium Communication[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-10. DOI： 10.12263/DZXB.20260077.

摘要

气-海跨介质通信是实现空-海跨域高速互联的关键技术，蓝绿光波段（450~550 nm）凭借其良好的海洋穿透特性，已成为水下无线光通信链路的理想传输窗口，为跨介质高速通信提供了重要支撑。然而，受大气、随机海面波浪及海水散射吸收的非线性耦合影响，跨介质信道呈现出显著的非平稳性与强时变特征。在此类快速时变信道环境中，依赖信道准静态假设的传统固定编码调制策略，会因为与实际信道失配而导致系统误码性能下降。针对这一问题，本文提出一种气-海跨介质通信信道特征动态学习传输方案，通过构建“周期性探测-动态学习-重构”的闭环反馈机制，使编码结构能够随海况变化而动态优化。具体而言，发送端周期性地发送预先设计好的导频序列，接收端利用该导频信号进行信道估计，实时统计当前信道的瞬时特征，进而生成与当前海况最匹配的极化码构造方案；随后通过反馈链路将该构造信息回传至发送端，发送端据此完成编码重构，对待传信息序列实施自适应极化编码。该闭环反馈过程周期性进行，确保编码重构紧紧跟随跨介质时变信道的非平稳性变化，从而克服了固定编码策略与真实物理信道失配的问题。为验证系统方案的性能，搭建了涵盖大气、气-海界面、海水介质的跨介质通信信道，选取了Jerlov IB、Jerlov II、Jerlov Ⅲ三种典型水质，分别模拟由弱到强的海水吸收与散射环境。仿真结果表明，所提信道特征动态学习策略能够适应当前海水介质的变化，与未编码系统相比，在相同Jerlov水质类型下采用极化编码可获得显著的编码增益，误比特率（Bit Error Rate， BER）明显降低。与3GPP-5G标准中采用的固定极化码构造策略相比，所提出的信道特征动态学习策略在三种不同Jerlov水质条件下均实现了显著的性能提升，且随着水体浑浊度增加、信道条件恶化，性能增益越高。上述结果证明，基于信道特征动态学习的自适应编码传输能够感知并适应海水介质的动态改变，为构建高可靠性的气-海跨介质自适应通信系统提供了坚实的理论依据与技术支撑。

Abstract

Air-sea cross-medium communication is a crucial technology for enabling high-speed air-sea cross-domain interconnection. Owing to its excellent oceanic penetration characteristics

the blue-green light band (450 nm~550 nm) has emerged as an ideal transmission window for underwater wireless optical communication links

providing vital support for high-speed cross-medium communication. However

subjected to the nonlinear coupling effects of the atmosphere

random sea surface waves

and seawater scattering and absorption

the cross-medium channel exhibits significant non-stationarity and strong time-varying characteristics. In such rapidly time-varying channel environments

traditional fixed coding and modulation strategies

which rely on quasi-static channel assumptions

suffer from degraded system error performance due to mismatches with the actual channel conditions. To address this issue

this paper proposes a transmission scheme based on dynamic channel feature learning for air-sea cross-medium communication. By constructing a closed-loop feedback mechanism of "periodic probing—dynamic learning—reconstruction

" the coding structure can be dynamically optimized in response to varying sea states. Specifically

the transmitter periodically transmits pre-designed pilot sequences. The receiver utilizes these pilot signals for channel estimation

compiles real-time statistics on the instantaneous characteristics of the current channel

and subsequently generates a polar code construction scheme that optimally matches the prevailing sea conditions. This construction information is then fed back to the transmitter via a feedback link. Based on this information

the transmitter executes code reconstruction and applies adaptive polar coding to the information sequence to be transmitted. This closed-loop feedback process operates periodically

ensuring that the code reconstruction closely tracks the non-stationary variations of the cross-medium time-varying channel

thereby overcoming the mismatch between fixed coding strategies and the actual physical channel. To verify the performance of the proposed system

a cross-medium communication channel model encompassing the atmosphere

the air-sea interface

and the seawater medium was established. Three typical water types—Jerlov IB

Jerlov II

and Jerlov Ⅲ—were selected to simulate oceanic environments with weak to strong absorption and scattering effects

respectively. Simulation results demonstrate that the proposed dynamic channel feature learning strategy can effectively adapt to variations in the seawater medium. Compared to uncoded systems

applying polar coding under the same Jerlov water type yields a significant coding gain

with the bit error rate (BER) markedly reduced. Furthermore

compared to the fixed polar code construction strategy adopted in the 3GPP-5G standard

the proposed dynamic channel feature learning strategy achieves substantial performance improvements across all three Jerlov water conditions. Notably

the performance gain increases as water turbidity rises and channel conditions deteriorate. These results validate that adaptive coded transmission based on dynamic channel feature learning can perceive and adapt to the dynamic changes of the seawater medium

providing a solid theoretical foundation and technical support for the development of highly reliable air-sea cross-medium adaptive communication systems.

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