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.

翻译：由于环境变化和信号不确定性，精确建模无线信道仍然是一个挑战。最近的神经网络能够学习射频（RF）信号传播模式，但它们独立处理射线上的每个体素，未考虑全局上下文或环境因素。本文提出了一种新方法，该方法学习完整射线的综合表征而非单个点，从而捕获更详细的环境特征。我们将Kolmogorov-Arnold网络（KAN）架构与Transformer模块相结合，在保持计算效率的同时，在真实和合成场景中实现了更好的性能。实验结果表明，该方法在各种场景下均优于现有方法。消融研究证实了我们模型每个组件对其有效性的贡献。额外实验为模型性能提供了清晰的解释。