Artificial Intelligence Empowered Channel Prediction: A New Paradigm for Propagation Channel Modeling

TL;DR

This paper introduces an AI-based paradigm for site-specific propagation channel prediction, integrating environmental data and physical knowledge to improve accuracy, generalization, and interpretability in wireless communication modeling.

Contribution

It presents a comprehensive AI-driven framework for propagation channel prediction, incorporating novel inference strategies, transfer learning, and explainable AI techniques.

Findings

Achieved an average path loss prediction RMSE of ~4 dB.

Reduced training time by 60-75%.

Demonstrated high-fidelity, generalizable channel prediction.

Abstract

This paper proposes a novel paradigm centered on Artificial Intelligence (AI)-empowered propagation channel prediction to address the limitations of traditional channel modeling. We present a comprehensive framework that deeply integrates heterogeneous environmental data and physical propagation knowledge into AI models for site-specific channel prediction, which referred to as channel inference. By leveraging AI to infer site-specific wireless channel states, the proposed paradigm enables accurate prediction of channel characteristics at both link and area levels, capturing spatio-temporal evolution of radio propagation. Some novel strategies to realize the paradigm are introduced and discussed, including AI-native and AI-hybrid inference approaches. This paper also investigates how to enhance model generalization through transfer learning and improve interpretability via explainable AI techniques. Our approach demonstrates significant practical efficacy, achieving an average path loss prediction root mean square error (RMSE) of $\sim$ 4 dB and reducing training time by 60\%-75\%. This new modeling paradigm provides a foundational pathway toward high-fidelity, generalizable, and physically consistent propagation channel prediction for future communication networks.