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

TL;DR

This paper introduces a novel 3D dynamic radio map prediction framework using Vision Transformers to model and forecast real-time power variations in low-altitude wireless networks with UAVs, enhancing network optimization.

Contribution

It presents a new ViT-based model for real-time 3D radio map prediction, capturing spatio-temporal dynamics in non-stationary UAV networks.

Findings

Accurately models fast-varying power dynamics.

Outperforms baseline models in reconstruction accuracy.

Effective in short-term power prediction.

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.