# Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing

arXiv: https://arxiv.org/abs/2607.00887 · HTML: https://mlsanity.com/paper/2607.00887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 60 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 29 of 29)

- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [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)
- [GAC-KAN: An Ultra-Lightweight GNSS Interference Classifier for GenAI-Powered Consumer Edge Devices](https://mlsanity.com/paper/2602.11186/) (2026)
- [Generative CKM Construction using Partially Observed Data with Diffusion Model](https://mlsanity.com/paper/2412.14812/) (2024)
- [SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification](https://mlsanity.com/paper/2601.12791/) (2026)
- [PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition](https://mlsanity.com/paper/2601.12798/) (2026)
- [JSR-GFNet: Jamming-to-Signal Ratio-Aware Dynamic Gating for Interference Classification in future Cognitive Global Navigation Satellite Systems](https://mlsanity.com/paper/2602.00042/) (2026)
- [Aerial Base Station Placement via Propagation Radio Maps](https://mlsanity.com/paper/2301.04966/) (2023)
- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)

## Built on by (most cited first, 1 of 1)

- [SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition](https://mlsanity.com/paper/2609.34875/) (2026)

## Abstract (arXiv)

Low-altitude Unmanned Aerial Vehicles (UAVs) often need to infer channel knowledge across a range of heights from only sparse observations collected at a few altitude layers. To address this challenge, this paper studies height-conditioned cross-height channel knowledge map (CKM) prediction for UAV-assisted communications in geometry-rich urban environments. We develop a geometry-aware conditional prediction framework that combines urban scene priors, sparse multi-altitude observations, and target-height descriptors to reconstruct dense CKMs at unobserved target heights. An uncertainty head is further introduced to characterize prediction confidence and to support cost-aware online UAV sensing under motion and safety constraints. Experiments on a layered aerial CKM benchmark show that the proposed Feature Pyramid Network (FPN)-Transformer achieves the best overall performance under both unseen-scene zero-shot and legacy patch-random protocols, reducing the Root Mean Square Error (RMSE) to 5.347dB and 1.111dB, respectively, compared with 6.937dB and 1.221dB for the strongest baseline 3D-RadioDiff. Moreover, after applying our unseen-scene few-shot adaptation, the RMSE further decreases from 5.347dB in zero-shot prediction to 3.518dB with 10-shot two-height support, while the uncertainty-guided cost-aware sensing policy improves active reconstruction from 6.94dB at initialization to 4.79dB at sensing budget 40, outperforming uncertainty-only sensing at 5.08dB and random aerial sampling at 5.84dB.