RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data

• URL: http://arxiv.org/abs/2602.18744v1
• Date: Sat, 21 Feb 2026 07:50:05 GMT
• Title: RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data
• Authors: Junshen Chen, Angzi Xu, Zezhong Zhang, Shiyao Zhang, Junting Chen, Shuguang Cui,
• Abstract summary: Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks.<n>Deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation.<n>We present the RadioGen3D framework to capture essential 3D signal propagation characteristics and antenna polarization effects.
• Score: 62.63849426834315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation (RME), most existing DL approaches are confined to 2D near-ground scenarios. They often fail to capture essential 3D signal propagation characteristics and antenna polarization effects, primarily due to the scarcity of 3D data and training challenges. To address these limitations, we present the RadioGen3D framework. First, we propose an efficient data synthesis method to generate high-quality 3D radio map data. By establishing a parametric target model that captures 2D ray-tracing and 3D channel fading characteristics, we derive realistic coefficient combinations from minimal real measurements, enabling the construction of a large-scale synthetic dataset, Radio3DMix. Utilizing this dataset, we propose a 3D model training scheme based on a conditional generative adversarial network (cGAN), yielding a 3D U-Net capable of accurate RME under diverse input feature combinations. Experimental results demonstrate that RadioGen3D surpasses all baselines in both estimation accuracy and speed. Furthermore, fine-tuning experiments verify its strong generalization capability via successful knowledge transfer.

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