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

• URL: http://arxiv.org/abs/2511.19019v1
• Date: Mon, 24 Nov 2025 11:47:17 GMT
• Title: 3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks
• Authors: Nguyen Duc Minh Quang, Chang Liu, Huy-Trung Nguyen, Shuangyang Li, Derrick Wing Kwan Ng, Wei Xiang,
• Abstract summary: Low-altitude wireless networks (LAWN) are rapidly expanding with the growing deployment of unmanned aerial vehicles (UAVs)<n>Reliable connectivity remains a critical yet challenging task due to three-dimensional (3D) mobility, time-varying user density, and limited power budgets.<n>We propose a 3D dynamic radio map (3D-altitude) framework that learns and predicts the evolution of received power.
• Score: 52.73015408160948
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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