# Channel Knowledge Map Construction via Guided Flow Matching

arXiv: https://arxiv.org/abs/2601.06156 · HTML: https://mlsanity.com/paper/2601.06156/

## Key facts

- Cited by 2 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 9 of its 16 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks](https://mlsanity.com/paper/2501.06604/) (2025)
- [CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors](https://mlsanity.com/paper/2504.17323/) (2025)
- [CKMImageNet: A Dataset for AI-Based Channel Knowledge Map Towards Environment-Aware Communication and Sensing](https://mlsanity.com/paper/2504.09849/) (2025)
- [On the Secrecy-Sensing Optimization of RIS-assisted Full-Duplex Integrated Sensing and Communication Network](https://mlsanity.com/paper/2504.20912/) (2025)
- [Flow Matching-Based Active Learning for Radio Map Construction with Low-Altitude UAVs](https://mlsanity.com/paper/2509.13822/) (2025)

## Built on by (most cited first, 2 of 2)

- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)
- [Active Learning for Channel Knowledge Map Construction via Bayesian Inference Diffusion Models](https://mlsanity.com/paper/2606.29862/) (2026)

## Abstract (arXiv)

The efficient construction of accurate channel knowledge maps (CKMs) is crucial for unleashing the full potential of environment-aware wireless networks, yet it remains a difficult ill-posed problem due to the sparsity of available location-specific channel knowledge data. Although diffusion-based methods such as denoising diffusion probabilistic models (DDPMs) have been exploited for CKM construction, they rely on iterative stochastic sampling, rendering them too slow for real-time wireless applications. To bridge the gap between high fidelity and efficient CKM construction, this letter introduces a novel framework based on linear transport guided flow matching (LT-GFM). Deviating from the noise-removal paradigm of diffusion models, our approach models the CKM generation process as a deterministic ordinary differential equation (ODE) that follows linear optimal transport paths, thereby drastically reducing the number of required inference steps. We propose a unified architecture that is applicable to not only the conventional channel gain map (CGM) construction, but also the more challenging spatial correlation map (SCM) construction. To achieve physics-informed CKM constructions, we integrate environmental semantics (e.g., building masks) for edge recovery and enforce Hermitian symmetry for property of the SCM. Simulation results verify that LT-GFM achieves superior distributional fidelity with significantly lower Fr'echet Inception Distance (FID) and accelerates inference speed by a factor of 25 compared to DDPMs.