In this paper, we propose the geometric algebra-informed neural radiance fields (GAI-NeRF), a novel framework for wireless channel prediction that leverages geometric algebra attention mechanisms to capture ray-object interactions in complex propagation environments. Our approach incorporates global token representations, drawing inspiration from transformer architectures in language and vision domains, to aggregate learned spatial-electromagnetic features and enhance scene understanding. We identify limitations in conventional static ray tracing modules that hinder model generalization and address this challenge through a new ray tracing architecture. This design enables effective generalization across diverse wireless scenarios while maintaining computational efficiency. Experimental results demonstrate that GAI-NeRF achieves superior performance in channel prediction tasks by combining geometric algebra principles with neural scene representations, offering a promising direction for next-generation wireless communication systems. Moreover, GAI-NeRF greatly outperforms existing methods across multiple wireless scenarios. To ensure comprehensive assessment, we further evaluate our approach against multiple benchmarks using newly collected real-world indoor datasets tailored for single-scene downstream tasks and generalization testing, confirming its robust performance in unseen environments and establishing its high efficacy for wireless channel prediction.

翻译：本文提出基于几何代数框架的神经辐射场（GAI-NeRF），一种利用几何代数注意力机制捕捉复杂传播环境中射线-物体交互的新型无线信道预测框架。该框架借鉴语言与视觉领域Transformer架构的全局令牌表征思想，通过聚合学习到的空间-电磁特征增强场景理解。我们指出现有静态射线追踪模块在模型泛化方面的局限性，并通过新型射线追踪架构解决该问题。该设计在保持计算效率的同时，实现了对多样化无线场景的有效泛化。实验结果表明，GAI-NeRF通过将几何代数原理与神经场景表征相结合，在信道预测任务中取得优越性能，为下一代无线通信系统提供了具有前景的研究方向。此外，GAI-NeRF在多种无线场景中显著超越现有方法。为确保评估的全面性，我们进一步采用面向单场景下游任务与泛化测试的新采集真实室内数据集，对方法进行多基准评估，验证了其在未知环境中的稳健性能，充分确立了该方法在无线信道预测领域的高效性。