Abstract
Radio maps reflect the spatial distribution of signal strength and are essential for applications like smart cities, IoT, and wireless network planning. However, reconstructing accurate radio maps from sparse measurements remains challenging. Traditional interpolation and inpainting methods lack environmental awareness, while many deep learning approaches depend on detailed scene data, limiting generalization. To address this, we propose MARS, a Multi-scale Aware Radiomap Super-resolution method that combines CNNs and Transformers with multi-scale feature fusion and residual connections. MARS focuses on both global and local feature extraction, enhancing feature representation across different receptive fields and improving reconstruction accuracy. Experiments across different scenes and antenna locations show that MARS outperforms baseline models in both MSE and SSIM, while maintaining low computational cost, demonstrating strong practical potential.
Abstract (translated)
无线电地图反映了信号强度的空间分布，对于智慧城市、物联网和无线网络规划等应用至关重要。然而，从稀疏测量中重建准确的无线电地图仍然具有挑战性。传统插值和修复方法缺乏环境感知能力，而许多深度学习方法依赖于详细的场景数据，这限制了其泛化能力。为解决这些问题，我们提出了MARS（Multi-scale Aware Radiomap Super-resolution），这是一种结合CNNs（卷积神经网络）和Transformers，并采用多尺度特征融合与残差连接的方法。MARS注重全局和局部特征提取，增强了不同感受野内的特征表示，从而提高了重建的准确性。 跨不同场景和天线位置进行的实验表明，MARS在均方误差(MSE)和结构相似性(SSIM)指标上都优于基线模型，并且保持了较低的计算成本，展示了其强大的实用潜力。
URL
https://arxiv.org/abs/2506.04682
