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

Zhihan Zeng; Amir Hussain; Yue Xiu; Phee Lep Yeoh; Lu Chen; Zhongpei Zhang; Guan Gui

doi:10.48550/arxiv.2607.00887

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

Zhihan Zeng, Amir Hussain, Yue Xiu, Phee Lep Yeoh, Lu Chen, Zhongpei Zhang and Guan Gui

arXiv, Vol.1 July 2026

Cornell University

2026

DOI: https://doi.org/10.48550/arxiv.2607.00887

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Abstract

Computer Vision and Pattern Recognition

channel knowlege map

unmanned aerial vehicle

cross-height prediction

FPN-Transformer

uncertainty-guided sensing

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.

Details

Title: Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing
Authors: Zhihan Zeng - University of Electronic Science and Technology of China
Amir Hussain - King Fahd University of Petroleum and Minerals
Yue Xiu - University of Electronic Science and Technology of China
Phee Lep Yeoh - University of the Sunshine Coast
Lu Chen (Corresponding Author) - Anhui Science and Technology University
Zhongpei Zhang - University of Electronic Science and Technology of China
Guan Gui - Nanjing University of Posts and Telecommunications
Publication details: arXiv, Vol.1 July 2026
Publisher: Cornell University
Date published: 2026
DOI: 10.48550/arxiv.2607.00887
ISSN: 2331-8422
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991242326402621
Output Type: Preprint

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