RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data
This addresses the need for efficient 3D radio resource management in future networks, offering a domain-specific solution that is incremental by extending existing 2D deep learning methods to 3D.
The paper tackles the problem of generating 3D radio maps for 6G and low-altitude networks by proposing RadioGen3D, a framework that uses a large-scale synthetic dataset and a cGAN-based training scheme, resulting in improved estimation accuracy and speed compared to baselines.
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.