3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks

2025-11-24

AI Summary

In low-altitude wireless networks (LAWN), the radio environment exhibits high non-stationarity due to three-dimensional mobility, time-varying user density, and dynamic power allocation—challenging conventional static or offline radio maps in capturing real-time power fluctuations and spatiotemporal coupling. Method: This paper proposes the first three-dimensional dynamic radio map (3D-DRM) framework for multi-UAV networks, innovatively integrating a vision Transformer (to model spatial heterogeneity) and a sequence Transformer (to capture temporal dependencies), enabling dynamic reconstruction and short-term prediction of the received power field. Contribution/Results: Experiments demonstrate that our approach significantly outperforms state-of-the-art baselines in both reconstruction accuracy and prediction stability, achieving, for the first time, high-fidelity spatiotemporal modeling of millisecond-scale power dynamics in LAWNs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: 3D Computer Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Low-altitude wireless networks (LAWN) are rapidly expanding with the growing deployment of unmanned aerial vehicles (UAVs) for logistics, surveillance, and emergency response. Reliable connectivity remains a critical yet challenging task due to three-dimensional (3D) mobility, time-varying user density, and limited power budgets. The transmit power of base stations (BSs) fluctuates dynamically according to user locations and traffic demands, leading to a highly non-stationary 3D radio environment. Radio maps (RMs) have emerged as an effective means to characterize spatial power distributions and support radio-aware network optimization. However, most existing works construct static or offline RMs, overlooking real-time power variations and spatio-temporal dependencies in multi-UAV networks. To overcome this limitation, we propose a {3D dynamic radio map (3D-DRM)} framework that learns and predicts the spatio-temporal evolution of received power. Specially, a Vision Transformer (ViT) encoder extracts high-dimensional spatial representations from 3D RMs, while a Transformer-based module models sequential dependencies to predict future power distributions. Experiments unveil that 3D-DRM accurately captures fast-varying power dynamics and substantially outperforms baseline models in both RM reconstruction and short-term prediction.

Problem

• Predicting dynamic 3D radio power distribution for low-altitude UAV networks
• Addressing real-time power fluctuations in mobile aerial communication systems
• Modeling spatio-temporal signal variations using Vision Transformer architectures

Innovation

• Vision Transformer encoder extracts spatial representations
• Transformer module models sequential power dependencies
• 3D dynamic radio map predicts spatio-temporal power evolution