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基于多源域的无线电环境地图的重建,适应图形神经网络的回归

Xiaomin Wen¹, Shengliang Fang², Youchen Fan²

¹Graduate School, School of Space Information, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)

|April 27, 2024

概括

本研究引入了一个图形神经网络 (GNN) 模型,使用稀疏数据准确预测无线电环境地图 (REM). 拟议的GNN-MDAR模型提高了性能,特别是有限的标记数据.

科学领域:

人工智能的人工智能
信号处理信号处理
无线通信无线通信

背景情况:

图形神经网络 (GNN) 擅长处理非结构化数据,但需要准确的图形结构和大型数据集.
由于复杂的空间无线电波传播和数据稀疏性,无线电环境地图 (REM) 预测具有挑战性.
现有的方法在域调整和准确的图形结构学习方面遇到了困难.

研究的目的:

开发一种基于GNN的新型模型,用于准确的REM预测和完成,使用多源域调整.
解决构建精确的空间图形结构和特征分布不匹配的光谱数据的挑战.
提高GNN在REM预测任务中的性能,特别是当标记数据稀缺时.

主要方法:

提出了一个多源域适应性GNN回归 (GNN-MDAR) 模型.
实现了图形结构对齐模块,以学习共享的跨域无线电传播结构.
引入了一个空间分布匹配模块,以减少跨空间网格的特征分布差异.

主要成果:

与四种基准方法相比,GNN-MDAR模型在REM预测方面表现出更高的准确性.
该模型的有效性在目标域中有限的参考信号接收功率 (RSRP) 标签数据的场景中尤其明显.
对比模拟实验验证了GNN-MDAR在测量REMs数据集上的性能.

结论:

该GNN-MDAR模型通过利用多源域调整有效地预测和完成REMs.
拟议的方法增强了域不变性,并减轻了特征分布不匹配.
这项工作为在数据稀缺的环境中准确预测REM提供了有希望的解决方案.

关键词:

图形神经网络的神经网络不变的推理推理.潜伏空间是一个隐藏空间.多源域调整多源域调整无线电环境地图无线电环境地图空间分布匹配匹配的空间分布.

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