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

TL;DR

This paper introduces GAI-NeRF, a novel neural framework that uses geometric algebra and transformer-inspired attention to improve wireless channel prediction across diverse environments.

Contribution

The paper presents GAI-NeRF, a new geometric algebra-based neural radiance field framework that enhances generalization and efficiency in wireless channel prediction tasks.

Findings

GAI-NeRF outperforms existing methods in multiple wireless scenarios.

The approach demonstrates robust generalization to unseen environments.

Experimental results confirm superior accuracy in real-world indoor datasets.

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.