# 6G Wireless Channel Measurements and Models: Trends and Challenges

arXiv: https://arxiv.org/abs/2012.06381 · HTML: https://mlsanity.com/paper/2012.06381/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 10 of 10)

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- [The Role, Trends, and Applications of Machine Learning in Undersea Communication: A Bangladesh Perspective](https://mlsanity.com/paper/2503.00669/) (2025)
- [Simulation of machine learning-based 6G systems in virtual worlds](https://mlsanity.com/paper/2204.09518/) (2022)
- [EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes](https://mlsanity.com/paper/2405.17366/) (2024)
- [Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling](https://mlsanity.com/paper/2603.01655/) (2026)

## Abstract (arXiv)

In this article, we first present our vision on the application scenarios, performance metrics, and potential key technologies of the sixth generation (6G) wireless communication networks. Then, 6G wireless channel measurements, characteristics, and models are comprehensively surveyed for all frequency bands and all scenarios, focusing on millimeter wave (mmWave), terahertz (THz), and optical wireless communication channels under all spectrums, satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios, and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of things (IoT) communication channels under full application scenarios. Future research challenges on 6G channel measurements, a general standard 6G channel model framework, channel measurements and models for intelligent reflection surface (IRS) based 6G technologies, and artificial intelligence (AI) enabled channel measurements and models are also given.