Research on key technologies of multi-source fusion digital twinning for wireless channels in low-altitude networks

Wang Dong; Chen Wenjun; Yang Mengke; Li Hang

doi:10.11959/j.issn.1000-0801.DXKX260198

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Research on key technologies of multi-source fusion digital twinning for wireless channels in low-altitude networks

更新时间：2026-06-23

- Research on key technologies of multi-source fusion digital twinning for wireless channels in low-altitude networks
- Telecommunications Science (2026)
- 作者机构：
  
  中兴通讯股份有限公司，陕西西安 710114
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260198
  CLC： TN92
- Received：27 March 2026，
  
  Revised：2026-04-20，
  
  Accepted：23 June 2026，
- 稿件说明：
移动端阅览
Wang Dong, Chen Wenjun, Yang Mengke, et al. Research on key technologies of multi-source fusion digital twinning for wireless channels in low-altitude networks[J/OL]. Telecommunications Science, 2026.
DOI：

Wang Dong, Chen Wenjun, Yang Mengke, et al. Research on key technologies of multi-source fusion digital twinning for wireless channels in low-altitude networks[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260198.

摘要

随着低空经济纳入国家战略性新兴产业，无人机规模化应用催生了对低空网络高精度覆盖规划的迫切需求。然而，传统规划依赖飞测数据与专家经验，面临多源异构数据融合不足、环境-天线-信道分离建模导致精度损失、以及深度学习可解释性缺失等瓶颈。针对上述挑战，提出一种面向低空网络无线信道的多源融合孪生架构，构建数据感知与采集、数据处理与融合、孪生模型构建与更新、孪生驱动规划及孪生可视化等五大模块。该架构通过地物环境、物理站点、天线波束、无线信道四层孪生体的逐层构建，实现从几何场景到电磁传播的高保真映射。关键技术包括：基于地形标签感知的Grid-to-TIN DEM快速转换算法、高贴合度体框拟合的工参自动提取方法、基于权值文件的波束动态孪生模型，以及融合射线追踪与可学习天线方向图的数据-知识双驱动信道建模范式。基于实测25 000个信道样本的验证表明，相较于传统双射线模型与纯射线追踪方法，所提信道孪生模型的RSRP预测均方根误差（RMSE）分别降低24.3%和42.2%，平均绝对误差（MAE）分别降低30.7%和47.3%，验证了多源融合对覆盖预测精度的显著提升。该研究为低空智联网络的精准站址规划、三维波束优化及动态干扰协调提供了理论依据与技术支撑，推动了信道建模向智能融合范式的演进。

Abstract

With the low-altitude economy being designated as a national strategic emerging industry

the large-scale deployment of unmanned aerial vehicles has created an urgent demand for high-precision coverage planning in low-altitude networks. However

conventional planning methods relying on flight measurement data and empirical expertise face critical bottlenecks

including insufficient multi-source heterogeneous data fusion

accuracy degradation caused by decoupled modeling of environment

antenna

and channel

and the lack of interpretability in deep learning approaches. To address these challenges

this paper proposes a multi-source fusion digital twin architecture for wireless channels in low-altitude networks

comprising five core modules: data sensing and acquisition

data processing and fusion

digital twin model construction and updating

twin-driven network planning

and digital twin visualization. By hierarchically constructing four-layer digital twins—environmental terrain

physical sites

antenna beams

and wireless channels—the architecture achieves high-fidelity mapping from geometric scenarios to electromagnetic propagation. Key technologies include a terrain-label-aware rapid decoupling algorithm for Grid-to-TIN digital elevation model conversion

a high-fidelity bounding box fitting method for automatic engineering parameter extraction

a dynamic beam twinning model based on weight files

and a data-knowledge dual-driven channel twinning paradigm integrating ray tracing with learnable antenna patterns. Validation based on 25

000 measured channel samples demonstrates that the proposed channel twinning model reduces the Root Mean Square Error (RMSE) of Reference Signal Received Power (RSRP) prediction by 24.3% and 42.2% compared to traditional two-ray models and pure ray tracing methods

respectively

with Mean Absolute Error (MAE) reductions of 30.7% and 47.3%. These results verify the significant accuracy improvement achieved through multi-source fusion. This study provides theoretical foundations and technical support for precise site planning

three-dimensional beam optimization

and dynamic interference coordination in low-altitude intelligent networks

advancing channel modeling toward an intelligent fusion paradigm.

关键词

Keywords

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