High-Resolution three-dimensional (3D) radio maps (RMs) provide rich information about the radio landscape that is essential to a myriad of wireless applications in the future wireless networks. Although deep learning (DL) methods have shown their effectiveness in RM construction, existing approaches require massive high-resolution 3D RM samples in the training dataset, the acquisition of which is labor-intensive and time-consuming in practice. In this paper, our goal is to devise a data-friendly high-resolution 3D RM construction solution via training over a hybrid dataset, wherein the RMs associated with a small fraction of environment maps (EMs) are of high-resolution, while those corresponding to the majority of EMs are of low-resolution. To this end, we propose a Data-Friendly 3D Radio Map Estimator (DF-3DRME), which comprises two processing stages. Specifically, in the first stage, we leverage the abundant low-resolution 3D RM samples to train a neural network, termed the LR-Net, for predicting the low-resolution 3D RM from the input EM, which provides a coarse characterization of the spatial radio propagation. In the second stage, we employ an advanced super-resolution network, termed the SR-Net, to upscale the predicted low-resolution 3D RM to its high-resolution counterpart. Unlike the LR-Net, the SR-Net can be effectively trained with only the limited high-resolution 3D RM samples available in the hybrid dataset. Experimental results demonstrate that the proposed framework achieves compelling reconstruction performance with only 4% of the EMs in the dataset having high-resolution 3D RM labels, which significantly reduces data acquisition overhead and facilitates practical deployment.

翻译：高分辨率三维（3D）无线电地图（RMs）可提供关于无线电环境丰富信息，这对未来无线网络中众多无线应用至关重要。尽管深度学习方法在RM构建中已展现出有效性，但现有方法要求训练数据集中包含大量高分辨率3D RM样本，而在实践中获取这些样本耗时耗力。本文旨在通过混合数据集训练，设计一种数据友好的高分辨率3D RM构建方案——其中仅少部分环境地图（EMs）对应高分辨率RM，而大部分EMs仅对应低分辨率RM。为此，我们提出数据友好型3D无线电地图估计器（DF-3DRME），该框架包含两个处理阶段。具体而言，第一阶段利用丰富的低分辨率3D RM样本训练神经网络（称为LR-Net），用于从输入EM中预测低分辨率3D RM，从而提供空间无线电传播的粗粒度表征。第二阶段采用先进超分辨率网络（称为SR-Net），将预测的低分辨率3D RM提升至对应的高分辨率版本。与LR-Net不同，SR-Net仅需利用混合数据集中有限的高分辨率3D RM样本即可有效训练。实验结果表明，当数据集中仅4%的EM具有高分辨率3D RM标签时，所提框架仍能实现具有竞争力的重建性能，显著降低了数据获取成本并便于实际部署。