A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction

• URL: http://arxiv.org/abs/2604.11983v1
• Date: Mon, 13 Apr 2026 19:20:29 GMT
• Title: A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction
• Abstract summary: We propose the geometric algebra-informed neural radiance fields (GAI-NeRF) for wireless channel prediction.<n>Our approach incorporates global token representations, drawing inspiration from transformer architectures in language and vision domains.<n> Experimental results demonstrate that GAI-NeRF achieves superior performance in channel prediction tasks.
• Score: 28.17627433640575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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