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.
@article{arxiv.2506.04682,
title = {MARS: Radio Map Super-resolution and Reconstruction Method under Sparse Channel Measurements},
author = {Chuyun Deng and Na Liu and Wei Xie and Lianming Xu and Li Wang},
journal= {arXiv preprint arXiv:2506.04682},
year = {2025}
}
Comments
The authors withdraw this submission to substantially revise the introduction and experimental sections and incorporate new content. The manuscript has not been submitted or published elsewhere. A revised version may be submitted in the future