Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.19019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912801347338240 |
|---|---|
| author | Quang, Nguyen Duc Minh Liu, Chang Nguyen, Huy-Trung Li, Shuangyang Ng, Derrick Wing Kwan Xiang, Wei |
| author_facet | Quang, Nguyen Duc Minh Liu, Chang Nguyen, Huy-Trung Li, Shuangyang Ng, Derrick Wing Kwan Xiang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks Quang, Nguyen Duc Minh Liu, Chang Nguyen, Huy-Trung Li, Shuangyang Ng, Derrick Wing Kwan Xiang, Wei Machine Learning 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. |
| title | 3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.19019 |