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

Ruisi He, Mi Yang, Zhengyu Zhang, Bo Ai, Zhangdui Zhong

TL;DR

This paper presents 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, and demonstrates significant practical efficacy.

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.

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

TL;DR

This paper presents 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, and demonstrates significant practical efficacy.

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 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.
Paper Structure (14 sections, 1 equation, 11 figures)

This paper contains 14 sections, 1 equation, 11 figures.

Table of Contents

  1. Introduction
  2. Physical Environment Representation and Extraction
  3. Characterization of Environmental Features
  4. Environmental Granularity
  5. Feature Fusion and Physics Enhancement
  6. Mapping Environmental Features to Propagation Mechanisms
  7. AI-Empowered Channel Inference
  8. Channel Inference Paradigm
  9. Model Architecture
  10. Channel Inference Results
  11. Generalization and Interpretability
  12. Model Generalization
  13. Model Interpretability
  14. Conclusion

Figures (11)

  • Figure 1: Hierarchical framework of environmental feature extraction for AI-based channel modeling.
  • Figure 2: A physics-inspired framework for investigating 3D environmental granularity.
  • Figure 3: Channel reconstruction error under different 3D environmental granularities. The absolute error represents the deviation between the ground truth and predicted values. Increasing voxel precision leads to a monotonic reduction in error by providing more accurate geometric priors for ray interaction modeling.
  • Figure 4: Path loss prediction performance using visual sensing.
  • Figure 5: Impact of fusing 3D physical information on channel prediction. Material: Material properties, including conductivity, relative permittivity, and scattering coefficient of the surface. Normal: Normal vector used to describe the orientation of scene surface. The normal vector, together with incident angle, determines the outgoing directions of reflected and scattered rays.
  • ...and 6 more figures