{"ID":2837507,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19019","arxiv_id":"2511.19019","title":"3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks","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.","short_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 pow...","url_abs":"https://arxiv.org/abs/2511.19019","url_pdf":"https://arxiv.org/pdf/2511.19019v2","authors":"[\"Nguyen Duc Minh Quang\",\"Chang Liu\",\"Huy-Trung Nguyen\",\"Shuangyang Li\",\"Derrick Wing Kwan Ng\",\"Wei Xiang\"]","published":"2025-11-24T11:47:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}