Generative CKM Construction using Partially Observed Data with Diffusion Model
On the map: Diffusion models → Diffusion for compression and wireless
Abstract (arXiv)
Channel knowledge map (CKM) is a promising technique that enables environment-aware wireless networks by utilizing location-specific channel prior information to improve communication and sensing performance. A fundamental problem for CKM construction is how to utilize partially observed channel knowledge data to reconstruct a complete CKM for all possible locations of interest. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with generative artificial intelligence (AI), in this paper, we propose generative CKM construction method using partially observed data by solving inverse problems with diffusion models. Simulation results show that the proposed method significantly improves the performance of CKM construction compared with benchmarking schemes.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. Builds on 8 of its 17 references that are in the map. First posted on arXiv 19 Dec 2024.
Builds on
8 of 17 references.
Closest ancestors · 1
Same direction, newest first
- 2024RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction16 Aug
Other references · 7
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2019RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networkscited by 126
- 2023A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6Gcited by 61
- 2022RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Networkcited by 50
- 2023How Much Data is Needed for Channel Knowledge Map Construction?cited by 14
- 2023A Tutorial on Near-Field XL-MIMO Communications Towards 6Gcited by 7
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priorscited by 6
- 2026Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selectioncited by 3
- 2026Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensingcited by 1
- 2026Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completioncited by 0
- 2026Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion2 Jul 2026
- 2026Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing1 Jul 2026
- 2026Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection7 Apr 2026
- 2025CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors24 Apr 2025
Similar, not cited
Close in wording, no citation either way
- 2025Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations
- 2026Channel Knowledge Map Construction via Guided Flow Matching
- 2025BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks ↗
- 2026Active Learning for Channel Knowledge Map Construction via Bayesian Inference Diffusion Models
- 2024Generative Diffusion Models for High Dimensional Channel Estimation
- 2025RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey
- 2026BeamCKMDiff: Beam-Aware Channel Knowledge Map Construction via Diffusion Transformer