用KAN+Transformer建模完整射线，提升无线场预测精度与可解释性
An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction
- 以整条射线为单位建模，融合全局环境特征
- 在真实与合成场景中均超越现有方法，计算效率高
- 提供清晰性能解释，适合无线系统设计者使用
由于环境变化和信号不确定性，精准建模无线信道仍具挑战。现有神经网络通常独立处理每一体素，忽略全局上下文与环境因素。本文提出一种新方法，学习完整射线的综合表征，捕捉更详细的环境特征。将柯尔莫哥洛夫-阿诺德网络（KAN）与变换器模块结合，在真实与合成场景中均实现更优性能，同时保持计算高效。实验表明该方法在多种场景下优于现有方法。消融实验确认模型各组件均有效贡献。额外实验提供了模型性能的清晰解释。
原文摘要 · Abstract (English)
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.
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