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

arXiv: https://arxiv.org/abs/2602.18744 · HTML: https://mlsanity.com/paper/2602.18744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [Deep Completion Autoencoders for Radio Map Estimation](https://mlsanity.com/paper/2005.05964/) (2020)
- [Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography](https://mlsanity.com/paper/2202.03269/) (2022)
- [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024)
- [RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication](https://mlsanity.com/paper/2507.12166/) (2025)
- [Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models](https://mlsanity.com/paper/2105.00177/) (2021)
- [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024)
- [UAV-aided Radio Map Construction Exploiting Environment Semantics](https://mlsanity.com/paper/2107.10574/) (2021)
- [Integrated Interpolation and Block-term Tensor Decomposition for Spectrum Map Construction](https://mlsanity.com/paper/2402.17138/) (2024)
- [Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN](https://mlsanity.com/paper/2402.02729/) (2024)
- [BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems](https://mlsanity.com/paper/2511.18376/) (2025)
- [Empowering Intelligent Low-altitude Economy with Large AI Model Deployment](https://mlsanity.com/paper/2505.22343/) (2025)
- [Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction](https://mlsanity.com/paper/2601.19216/) (2026)
- [Data-Driven Radio Environment Map Estimation Using Graph Neural Networks](https://mlsanity.com/paper/2407.07713/) (2024)
- [A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization](https://mlsanity.com/paper/2501.04237/) (2025)

## Abstract (arXiv)

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.