UAV-aided Radio Map Construction Exploiting Environment Semantics
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Abstract (arXiv)
This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2021.
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Citing papers per year: 2024: 4; 2025: 4; 2026: 3.
11 papers.
- 2024Fast and Accurate Cooperative Radio Map Estimation Enabled by GANcited by 7
- 2024Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learningcited by 6
- 2025Empowering Intelligent Low-altitude Economy with Large AI Model Deploymentcited by 3
- 2025Blind Construction of Angular Power Maps in Massive MIMO Networkscited by 3
- 2024Two-Stage Radio Map Construction with Real Environments and Sparse Measurementscited by 1
- 2024Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approachcited by 1
- 2025Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environmentscited by 1
- 2025Physics-Informed Neural Networks for MIMO Beam Map and Environment Reconstructioncited by 0
- 2026RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Datacited by 0
- 2026CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learningcited by 0
- 2026BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Managementcited by 0
- 2026BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management1 Sep 2026
- 2026CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning26 Mar 2026
- 2026RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data21 Feb 2026
- 2025Physics-Informed Neural Networks for MIMO Beam Map and Environment Reconstruction24 Oct 2025
- 2025Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments9 Oct 2025
- 2025Blind Construction of Angular Power Maps in Massive MIMO Networks8 Oct 2025
- 2025Empowering Intelligent Low-altitude Economy with Large AI Model Deployment28 May 2025
- 2024Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach30 Nov 2024
- 2024Two-Stage Radio Map Construction with Real Environments and Sparse Measurements8 Oct 2024
- 2024Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning1 Mar 2024
- 2024Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN5 Feb 2024