基于注意力增强特征调制的高效三维天线辐射方向图重构

周珂全; 余官定; 金敏玉; 雷志纯

doi:10.11959/j.issn.1000-0801.DXKX260317

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基于注意力增强特征调制的高效三维天线辐射方向图重构

研究与开发 | 更新时间：2026-09-30

- 基于注意力增强特征调制的高效三维天线辐射方向图重构
- Efficient 3D Antenna Radiation Pattern Reconstruction via Attention-Enhanced Feature Modulation
- 电信科学 2026年
- 作者机构：
  
  1.浙江大学信息与电子工程学院，浙江杭州 310027
  2.华信咨询设计研究院有限公司，浙江杭州 310000
- 作者简介：
  
  雷志纯，leizc.hx@chinaccs.cn
- 基金信息：
  
  国家自然科学基金资助项目(62471434)
- DOI：10.11959/j.issn.1000-0801.DXKX260317
  中图分类号： TN929.5
- 收稿：2026-05-18，
  
  修回：2026-07-09，
  
  录用：2026-08-20，
- 稿件说明：
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周珂全, 余官定, 金敏玉, 等. 基于注意力增强特征调制的高效三维天线辐射方向图重构[J/OL]. 电信科学, 2026.

ZHOU Kequan, YU Guanding, JIN Minyu, et al. Efficient 3D Antenna Radiation Pattern Reconstruction via Attention-Enhanced Feature Modulation[J/OL]. Telecommunications Science, 2026.
周珂全, 余官定, 金敏玉, 等. 基于注意力增强特征调制的高效三维天线辐射方向图重构[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260317.

ZHOU Kequan, YU Guanding, JIN Minyu, et al. Efficient 3D Antenna Radiation Pattern Reconstruction via Attention-Enhanced Feature Modulation[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260317.

摘要

针对无线通信仿真与网络规划中完整三维天线辐射方向图获取困难的问题，提出了一种基于注意力增强特征调制的高效三维天线辐射方向图重构方法。设计了轻量化稳健损失函数，以结构一致性约束替代现有深度学习方法的外部自编码器监督，降低额外训练负担。同时，引入双向交叉注意力与特征调制机制，对水平与垂直切片特征进行动态融合，从而显著提升全局重建的精度。此外，基于U-Net架构进一步改进了神经网络，在与基线同等参数量级下实现了更快的误差收敛和更高的重建精度。在多样化天线数据集上的仿真结果表明，该方法在均方根误差与结构相似性指标上均显著优于传统解析方法与现有人工智能基线，验证了该方法在天线辐射方向图重构方面的有效性。

Abstract

To address the difficulty of acquiring complete three-dimensional (3D) antenna radiation patterns for wireless simulation and network planning

an efficient reconstruction method based on attention-enhanced feature modulation was proposed. A lightweight robust loss function was designed

where structural consistency constraints were used to replace external autoencoder-based supervision

thereby reducing additional training overhead. Meanwhile

bidirectional cross-attention and feature modulation mechanisms were introduced to dynamically fuse horizontal and vertical slice features

thereby improving global reconstruction accuracy. Furthermore

the standard U-Net architecture was refined to achieve faster error convergence and higher reconstruction accuracy under a comparable parameter budget. Extensive simulations on a diverse antenna dataset demonstrate that the proposed method consistently outperforms both traditional analytical methods and existing artificial intelligence (AI) baselines in root mean square error (RMSE) and structural similarity index (SSIM) metrics

confirming its effectiveness for antenna radiation pattern reconstruction.

关键词

Keywords

references

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