# Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection arXiv: https://arxiv.org/abs/2604.05788 ยท HTML: https://mlsanity.com/paper/2604.05788/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 47 of its 78 references that are in the map. - First posted on arXiv 7 Apr 2026. ## Builds on (most cited first, 30 of 47) - [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024) - [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019) - [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023) - [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024) - [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020) - [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021) - [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020) - [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024) - [Learning Power Spectrum Maps from Quantized Power Measurements](https://mlsanity.com/paper/1606.02679/) (2016) - [Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence](https://mlsanity.com/paper/1808.01053/) (2018) - [Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models](https://mlsanity.com/paper/2105.00177/) (2021) - [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022) - [How Much Data is Needed for Channel Knowledge Map Construction?](https://mlsanity.com/paper/2312.06966/) (2023) - [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024) - [Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map](https://mlsanity.com/paper/2305.15526/) (2023) - [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024) - [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022) - [Adaptive Bayesian Radio Tomography](https://mlsanity.com/paper/1804.02084/) (2018) - [Location-free Spectrum Cartography](https://mlsanity.com/paper/1812.11539/) (2018) - [Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information](https://mlsanity.com/paper/1404.0979/) (2014) - [Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map based Approach](https://mlsanity.com/paper/2009.12804/) (2020) - [Robust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information](https://mlsanity.com/paper/2409.15894/) (2024) - [BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems](https://mlsanity.com/paper/2511.18376/) (2025) - [Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI](https://mlsanity.com/paper/2501.15410/) (2025) - [Flexible WMMSE Beamforming for MU-MIMO Movable Antenna Communications](https://mlsanity.com/paper/2503.17718/) (2025) - [CKMImageNet: A Dataset for AI-Based Channel Knowledge Map Towards Environment-Aware Communication and Sensing](https://mlsanity.com/paper/2504.09849/) (2025) - [Distortion-Aware Hybrid Beamforming for Integrated Sensing and Communication](https://mlsanity.com/paper/2507.14018/) (2025) - [Robust Optimization for Movable Antenna-aided Cell-Free ISAC with Time Synchronization Errors](https://mlsanity.com/paper/2508.13818/) (2025) ## Built on by (most cited first, 3 of 3) - [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026) - [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026) - [SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition](https://mlsanity.com/paper/2609.34875/) (2026) ## Abstract (arXiv) Radio maps are important for environment-aware wireless communication, network planning, and radio resource optimization. However, dense radio map construction remains challenging when only a limited number of measurements are available, especially in complex urban environments with strong blockages, irregular geometry, and restricted sensing accessibility. Existing methods have explored interpolation, low-rank cartography, deep completion, and channel knowledge map (CKM) construction, but many of these methods insufficiently exploit explicit geometric priors or overlook the value of predictive uncertainty for subsequent sensing. In this paper, we study sparse gain radio map reconstruction from a geometry-aware and active sensing perspective. We first construct UrbanRT-RM, a controllable ray-tracing benchmark with diverse urban layouts, multiple base-station deployments, and multiple sparse sampling modes. We then propose GeoUQ-GFNet, a lightweight network that jointly predicts a dense gain radio map and a spatial uncertainty map from sparse measurements and structured scene priors. The predicted uncertainty is further used to guide active measurement selection under limited sensing budgets. Extensive experiments show that our proposed GeoUQ-GFNet method achieves strong and consistent reconstruction performance across different scenes and transmitter placements generated using UrbanRT-RM. Moreover, uncertainty-guided querying provides more effective reconstruction improvement than non-adaptive sampling under the same additional measurement budget. These results demonstrate the effectiveness of combining geometry-aware learning, uncertainty estimation, and benchmark-driven evaluation for sparse radio map reconstruction in complex urban environments.