MARS通过多尺度融合提升稀疏测量下的无线地图超分辨率重建精度
MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements
- 结合CNN与Transformer的多尺度特征融合架构
- 在多种场景和天线位置下实现更低的均方误差与更高结构相似性
- 兼顾高精度与低计算开销，适合实际部署
无线地图反映了信号强度的空间分布，对智慧城市、物联网及无线网络规划至关重要。然而，从稀疏测量中重建精确的无线地图仍具挑战：传统插值和修复方法缺乏环境感知，许多深度学习方法依赖详细场景数据，泛化能力受限。为此，本文提出MARS（Multi-scale Aware Radiomap Super-resolution），一种融合卷积神经网络与Transformer的多尺度特征融合方法，通过残差连接增强全局与局部特征提取，提升不同感受野下的表征能力。在多个场景与天线位置上的实验表明，MARS在均方误差（MSE）与结构相似性（SSIM）上均优于基线模型，同时保持较低计算成本，展现出良好的实际应用潜力。
原文摘要 · Abstract (English)
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.
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