# CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors arXiv: https://arxiv.org/abs/2504.17323 ยท HTML: https://mlsanity.com/paper/2504.17323/ ## Key facts - Cited by 6 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 31 of its 68 references that are in the map. - First posted on arXiv 24 Apr 2025. ## Placed in - Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/) ## Builds on (most cited first, 30 of 31) - [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) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016) - [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014) - [A Survey on Diffusion Models for Inverse Problems](https://mlsanity.com/paper/2410.00083/) (2024) - [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) - [Dataset of Pathloss and ToA Radio Maps With Localization Application](https://mlsanity.com/paper/2212.11777/) (2022) - [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025) - [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022) - [How Much Data is Needed for Channel Knowledge Map Construction?](https://mlsanity.com/paper/2312.06966/) (2023) - [Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks](https://mlsanity.com/paper/2501.06604/) (2025) - [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024) - [6G Wireless Channel Measurements and Models: Trends and Challenges](https://mlsanity.com/paper/2012.06381/) (2020) - [IMNet: Interference-Aware Channel Knowledge Map Construction and Localization](https://mlsanity.com/paper/2412.01279/) (2024) - [RMTransformer: Accurate Radio Map Construction and Coverage Prediction](https://mlsanity.com/paper/2501.05190/) (2025) - [CKMImageNet: A Dataset for AI-Based Channel Knowledge Map Towards Environment-Aware Communication and Sensing](https://mlsanity.com/paper/2504.09849/) (2025) - [Environment-Aware Beam Selection for IRS-Aided Communication with Channel Knowledge Map](https://mlsanity.com/paper/2111.11289/) (2021) - [Generative CKM Construction using Partially Observed Data with Diffusion Model](https://mlsanity.com/paper/2412.14812/) (2024) - [Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios](https://mlsanity.com/paper/2206.02442/) (2022) - [Channel Knowledge Map (CKM)-Assisted Multi-UAV Wireless Network: CKM Construction and UAV Placement](https://mlsanity.com/paper/2207.01931/) (2022) - [Prototyping and Experimental Results for Environment-Aware Millimeter Wave Beam Alignment via Channel Knowledge Map](https://mlsanity.com/paper/2403.08200/) (2024) - [Physics-Inspired Distributed Radio Map Estimation](https://mlsanity.com/paper/2502.00319/) (2025) - [Prototyping and Experimental Results for ISAC-based Channel Knowledge Map](https://mlsanity.com/paper/2408.06164/) (2024) - [Two-Stage Radio Map Construction with Real Environments and Sparse Measurements](https://mlsanity.com/paper/2410.18092/) (2024) ## Built on by (most cited first, 6 of 6) - [RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet](https://mlsanity.com/paper/2508.09140/) (2025) - [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025) - [Channel Knowledge Map Construction via Guided Flow Matching](https://mlsanity.com/paper/2601.06156/) (2026) - [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025) - [Active Learning for Channel Knowledge Map Construction via Bayesian Inference Diffusion Models](https://mlsanity.com/paper/2606.29862/) (2026) - [RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning](https://mlsanity.com/paper/2607.20909/) (2026) ## Abstract (arXiv) Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing with greatly enhanced performance, by offering location-specific channel prior information for future wireless networks. One fundamental problem for CKM-enabled wireless systems lies in how to construct high-quality and complete CKM for all locations of interest, based on only limited and noisy on-site channel knowledge data. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited and noisy observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with learned priors using generative artificial intelligence (AI), we propose CKMDiff, a conditional diffusion model that can be applied to perform various tasks for CKM constructions such as denoising, inpainting, and super-resolution, without having to know the physical environment maps or transceiver locations. Furthermore, we propose an environment-aware data augmentation mechanism to enhance the model's ability to learn implicit relations between electromagnetic propagation patterns and spatial-geometric features. Extensive numerical results are provided based on the CKMImageNet and RadioMapSeer datasets, which demonstrate that the proposed CKMDiff achieves state-of-the-art performance, outperforming various benchmark methods.