# Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations

arXiv: https://arxiv.org/abs/2512.02757 · HTML: https://mlsanity.com/paper/2512.02757/

## Key facts

- Cited by 1 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 26 of its 52 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Placed in

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

## Builds on (most cited first, 26 of 26)

- [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)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [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)
- [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)
- [A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables](https://mlsanity.com/paper/2006.01789/) (2020)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Dataset of Pathloss and ToA Radio Maps With Localization Application](https://mlsanity.com/paper/2212.11777/) (2022)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments](https://mlsanity.com/paper/2105.07158/) (2021)
- [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)
- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors](https://mlsanity.com/paper/2504.17323/) (2025)
- [RMTransformer: Accurate Radio Map Construction and Coverage Prediction](https://mlsanity.com/paper/2501.05190/) (2025)
- [REM-U-net: Deep Learning Based Agile REM Prediction with Energy-Efficient Cell-Free Use Case](https://mlsanity.com/paper/2309.11898/) (2023)
- [ReVeal: A Physics-Informed Neural Network for High-Fidelity Radio Environment Mapping](https://mlsanity.com/paper/2502.19646/) (2025)

## Built on by (most cited first, 1 of 1)

- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)

## Abstract (arXiv)

The ability to construct channel knowledge map (CKM) with high precision is essential for environment awareness in 6G wireless systems. However, most existing CKM construction methods formulate the task as an image super-resolution or generation problem, thereby employing models originally developed for computer vision. As a result, the generated CKMs often fail to capture the underlying physical characteristics of wireless propagation. In this paper, considering that acquiring channel observations incurs non-negligible time and cost, we focus on constructing CKM for large-scale fading scenarios without relying on prior observations, and we design three physics-based constraints to characterize the spatial distribution patterns of large-scale fading. By integrating these physical constraints with state-of-the-art diffusion model that possesses superior generative capability, a physics-inspired diffusion model for CKM construction is proposed. Following this motivation, we derive the loss function of the diffusion model augmented with physics-based constraint terms and further design the training and generation framework for the proposed physics-inspired CKM generation diffusion model. Extensive experiments show that our approach outperforms all existing methods in terms of construction accuracy. Moreover, the proposed model provides a unified and effective framework with strong potential for generating diverse, accurate, and physically consistent CKM.