# COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel Modeling

arXiv: https://arxiv.org/abs/2411.11798 · HTML: https://mlsanity.com/paper/2411.11798/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl](https://mlsanity.com/paper/2305.01582/) (2023)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling](https://mlsanity.com/paper/2111.12228/) (2021)
- [An Overview of Machine Learning Techniques for Radiowave Propagation Modeling](https://mlsanity.com/paper/2101.11760/) (2021)
- [White Paper on Radio Channel Modeling and Prediction to Support Future Environment-aware Wireless Communication Systems](https://mlsanity.com/paper/2309.17088/) (2023)

## Built on by (most cited first, 3 of 3)

- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling](https://mlsanity.com/paper/2503.13468/) (2025)
- [Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework](https://mlsanity.com/paper/2604.02396/) (2026)

## Abstract (arXiv)

Accurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, and computational complexity. The fundamental reason is that establishing a quantified and accurate mapping between physical environment and channel characteristics becomes increasing challenging for modern communication systems. Here, in the context of COST CA20120 Action, we evaluate and discuss the feasibility and implementation of using artificial intelligence (AI) for channel modeling, and explore where the future of this field lies. Firstly, we present a framework of AI-based channel modeling to characterize complex wireless channels. Then, we highlight in detail some major challenges and present the possible solutions: i) estimating the uncertainty of AI-based channel predictions, ii) integrating prior knowledge of propagation to improve generalization capabilities, and iii) interpretable AI for channel modeling. We present and discuss illustrative numerical results to showcase the capabilities of AI-based channel modeling.