Aerial Base Station Placement via Propagation Radio Maps
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Abstract (arXiv)
The deployment of aerial base stations (ABSs) on unmanned aerial vehicles (UAVs) presents a promising solution for extending cellular connectivity to areas where terrestrial infrastructure is overloaded, damaged, or absent. A pivotal challenge in this domain is to decide the locations of a set of ABSs to effectively serve ground-based users. Most existing approaches oversimplify this problem by assuming that the channel gain between two points is a function of solely distance and, sometimes, also the elevation angle. In turn, this paper leverages propagation radio maps to account for arbitrary air-to-ground channel gains. This methodology enables the identification of an approximately minimal set of locations where ABSs need to be deployed to ensure that all ground terminals achieve a target service rate, while adhering to backhaul capacity limitations and avoiding designated no-fly zones. Relying on a convex relaxation technique and the alternating direction method of multipliers (ADMM), this paper puts forth a solver whose computational complexity scales linearly with the number of ground terminals. Convergence is established analytically and an extensive set of simulations corroborate the merits of the proposed scheme relative to conventional methods.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jan 2023.
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Citing papers per year: 2026: 3.
3 papers.
- 2026Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selectioncited by 3
- 2026Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensingcited by 1
- 2026WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimizationcited by 0
- 2026WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimization16 Aug 2026
- 2026Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing1 Jul 2026
- 2026Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection7 Apr 2026