# A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

arXiv: https://arxiv.org/abs/2309.07460 · HTML: https://mlsanity.com/paper/2309.07460/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2023.

## Built on by (most cited first, 30 of 61)

- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [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)
- [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024)
- [Knowledge Base Enabled Semantic Communication: A Generative Perspective](https://mlsanity.com/paper/2311.12443/) (2023)
- [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)
- [Generative CKM Construction using Partially Observed Data with Diffusion Model](https://mlsanity.com/paper/2412.14812/) (2024)
- [Empowering Intelligent Low-altitude Economy with Large AI Model Deployment](https://mlsanity.com/paper/2505.22343/) (2025)
- [A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction](https://mlsanity.com/paper/2507.14186/) (2025)
- [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)
- [Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin](https://mlsanity.com/paper/2401.08023/) (2024)
- [Radio Map Estimation via Latent Domain Plug-and-Play Denoising](https://mlsanity.com/paper/2501.13472/) (2025)
- [Blind Construction of Angular Power Maps in Massive MIMO Networks](https://mlsanity.com/paper/2510.07071/) (2025)
- [Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels](https://mlsanity.com/paper/2512.04966/) (2025)
- [BeamCKMDiff: Beam-Aware Channel Knowledge Map Construction via Diffusion Transformer](https://mlsanity.com/paper/2601.10207/) (2026)
- [Deep Learning-Based CKM Construction with Image Super-Resolution](https://mlsanity.com/paper/2411.08887/) (2024)
- [Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction](https://mlsanity.com/paper/2411.11576/) (2024)
- [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)
- [Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach](https://mlsanity.com/paper/2412.00386/) (2024)
- [Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum Management](https://mlsanity.com/paper/2501.12853/) (2025)
- [Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach](https://mlsanity.com/paper/2505.07893/) (2025)
- [EnvCDiff: Joint Refinement of Environmental Information and Channel Fingerprints via Conditional Generative Diffusion Model](https://mlsanity.com/paper/2505.07894/) (2025)
- [Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting](https://mlsanity.com/paper/2506.12787/) (2025)
- [Bayesian-Driven Graph Reasoning for Active Radio Map Construction](https://mlsanity.com/paper/2508.09142/) (2025)
- [RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting](https://mlsanity.com/paper/2508.16849/) (2025)
- [Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations](https://mlsanity.com/paper/2512.02757/) (2025)
- [Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference](https://mlsanity.com/paper/2512.13701/) (2025)

## Abstract (arXiv)

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, e.g., the estimated channels or beams between base station (BS) and user equipment (UE), making it possible to better learn the local wireless environment. By exploiting this new opportunity and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, followed by the comparison of CKM with various existing channel inference techniques. Next, the main techniques for CKM construction are discussed, including both environment model-free and environment model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work.