An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction

2025-10-06

IEEE International Conference on Mobile Adhoc and Sensor Systems

AI Summary

This work addresses the challenge of accurate wireless channel modeling posed by environmental dynamics and signal uncertainty, where existing approaches struggle to effectively integrate global context with environmental factors. The paper proposes a novel method that, for the first time, combines Kolmogorov–Arnold Networks (KANs) with a Transformer architecture, replacing conventional voxel-based representations with a ray-level holistic representation to enable efficient and interpretable prediction of wireless radiance fields. Evaluated in both real-world and synthetic scenarios, the proposed approach consistently outperforms state-of-the-art methods. Ablation studies confirm the contribution of each component, while interpretability analyses provide insight into the origins of the model’s performance advantages.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web search models and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modeling wireless channels accurately remains a challenge due to environmental variations and signal uncertainties. Recent neural networks can learn radio frequency (RF) signal propagation patterns, but they process each voxel on the ray independently, without considering global context or environmental factors. Our paper presents a new approach that learns comprehensive representations of complete rays rather than individual points, capturing more detailed environmental features. We integrate a Kolmogorov-Arnold network (KAN) architecture with transformer modules to achieve better performance across realistic and synthetic scenes while maintaining computational efficiency. Our experimental results show that this approach outperforms existing methods in various scenarios. Ablation studies confirm that each component of our model contributes to its effectiveness. Additional experiments provide clear explanations for our model’s performance.

Problem

• wireless channel modeling
• radio frequency propagation
• environmental variations
• signal uncertainty
• ray representation

Innovation

• Kolmogorov-Arnold Network
• Wireless Channel Prediction
• Ray-level Representation
• Transformer Integration
• Explainable AI