# RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction

arXiv: https://arxiv.org/abs/2408.08593 · HTML: https://mlsanity.com/paper/2408.08593/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior](https://mlsanity.com/paper/2308.15070/) (2023)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [U-shaped Vision Mamba for Single Image Dehazing](https://mlsanity.com/paper/2402.04139/) (2024)
- [Frequency-Adaptive Dilated Convolution for Semantic Segmentation](https://mlsanity.com/paper/2403.05369/) (2024)
- [Toward Immersive Communications in 6G](https://mlsanity.com/paper/2303.05283/) (2023)
- [The First Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2310.07658/) (2023)
- [Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT](https://mlsanity.com/paper/2407.10979/) (2024)
- [Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach](https://mlsanity.com/paper/2306.08938/) (2023)
- [Uplink-aided High Mobility Downlink Channel Estimation over Massive MIMO-OTFS System](https://mlsanity.com/paper/2003.07045/) (2020)
- [Exploiting Radio Fingerprints for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2305.13635/) (2023)
- [Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization](https://mlsanity.com/paper/2407.03668/) (2024)
- [Wireless Communication via Double IRS: Channel Estimation and Passive Beamforming Designs](https://mlsanity.com/paper/2008.11439/) (2020)
- [Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach](https://mlsanity.com/paper/2312.02184/) (2023)

## Built on by (most cited first, 30 of 56)

- [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025)
- [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)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving](https://mlsanity.com/paper/2507.21423/) (2025)
- [RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction](https://mlsanity.com/paper/2504.14298/) (2025)
- [CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors](https://mlsanity.com/paper/2504.17323/) (2025)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.04672/) (2024)
- [RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet](https://mlsanity.com/paper/2508.09140/) (2025)
- [Generative CKM Construction using Partially Observed Data with Diffusion Model](https://mlsanity.com/paper/2412.14812/) (2024)
- [RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse](https://mlsanity.com/paper/2601.02790/) (2026)
- [U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach](https://mlsanity.com/paper/2603.06401/) (2026)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [BeamCKMDiff: Beam-Aware Channel Knowledge Map Construction via Diffusion Transformer](https://mlsanity.com/paper/2601.10207/) (2026)
- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)
- [iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization](https://mlsanity.com/paper/2511.20015/) (2025)
- [RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction](https://mlsanity.com/paper/2603.18865/) (2026)
- [Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM](https://mlsanity.com/paper/2506.04593/) (2025)
- [Non-Identical Diffusion Models in MIMO-OFDM Channel Generation](https://mlsanity.com/paper/2509.01641/) (2025)
- [RadioFlow: Efficient Radio Map Construction Framework with Flow Matching](https://mlsanity.com/paper/2510.09314/) (2025)
- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI](https://mlsanity.com/paper/2511.00494/) (2025)
- [3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2511.19019/) (2025)
- [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025)
- [Generative Spectrum Cartography: Unified Reconstruction and Active Sensing via Diffusion Models](https://mlsanity.com/paper/2512.20108/) (2025)
- [Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model](https://mlsanity.com/paper/2603.18853/) (2026)
- [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026)
- [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026)
- [ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions](https://mlsanity.com/paper/2503.18375/) (2025)

## Abstract (arXiv)

Radio map (RM) is a promising technology that can obtain pathloss based on only location, which is significant for 6G network applications to reduce the communication costs for pathloss estimation. However, the construction of RM in traditional is either computationally intensive or depends on costly sampling-based pathloss measurements. Although the neural network (NN)-based method can efficiently construct the RM without sampling, its performance is still suboptimal. This is primarily due to the misalignment between the generative characteristics of the RM construction problem and the discrimination modeling exploited by existing NN-based methods. Thus, to enhance RM construction performance, in this paper, the sampling-free RM construction is modeled as a conditional generative problem, where a denoised diffusion-based method, named RadioDiff, is proposed to achieve high-quality RM construction. In addition, to enhance the diffusion model's capability of extracting features from dynamic environments, an attention U-Net with an adaptive fast Fourier transform module is employed as the backbone network to improve the dynamic environmental features extracting capability. Meanwhile, the decoupled diffusion model is utilized to further enhance the construction performance of RMs. Moreover, a comprehensive theoretical analysis of why the RM construction is a generative problem is provided for the first time, from both perspectives of data features and NN training methods. Experimental results show that the proposed RadioDiff achieves state-of-the-art performance in all three metrics of accuracy, structural similarity, and peak signal-to-noise ratio. The code is available at https://github.com/UNIC-Lab/RadioDiff.