App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Deep learning–based dynamic UAV coverage prediction in urban environments

Zou Han
Liu Bin
Hu Haifeng
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0481

Abstract

Unmanned aerial vehicles (UAVs) play an important role in emergency communications, disaster relief, and hotspot coverage enhancement, where accurate prediction of communication performance at fixed ground receivers is crucial for deployment. To address this new scenario, this paper proposes a fast UAV coverage prediction method based on deep learning. To solve the problems of high computational cost of ray tracing and the limited accuracy of empirical models, it constructs a high-precision coverage database by combining real urban building data with ray-tracing simulations, and designs a deep convolutional network named BUD-U-Net. The network takes dual-channel inputs consisting of building contours and Gaussian-encoded ground receiver points, and outputs the received signal strength distribution with UAV positions as variables in an end-to-end manner. Experimental results show that the coverage prediction method performs well on the coverage metric, improving by about 4~11 percentage points over the slicing-based method and the scalar regression method, achieving the lowest pixel-level absolute error and a cross-scene MSE of about 0.014, clearly outperforming other methods. Meanwhile, it achieves a more balanced engineering trade-off among accuracy, real-time performance, and deployment cost, providing a feasible new modeling paradigm for dynamic UAV deployment and intelligent network planning.

Foundation Support

国家自然科学基金资助项目(62371245)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0481
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-24] Accepted Paper

Cite This Article

邹晗, 刘彬, 胡海峰. 基于深度学习的城市环境UAV动态覆盖预测 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0481. (Zou Han, Liu Bin, Hu Haifeng. Deep learning–based dynamic UAV coverage prediction in urban environments [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0481. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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