VoxelRF: Voxelized Radiance Field for Fast Wireless Channel Modeling

Wireless channel modeling in complex environments is crucial for modern communication system design and deployment. Traditional channel modeling approaches face challenges in balancing accuracy, efficiency, and scalability, while recent neural approaches such as neural radiance field (NeRF) suffer from long training and slow inference. To tackle these challenges, we propose voxelized radiance field (VoxelRF), a novel neural representation for wireless channel modeling that enables fast and accurate synthesis of spatial spectra of received signals. VoxelRF replaces the costly multilayer perceptron (MLP) used in NeRF-based methods with trilinear interpolation of voxel grid-based representation and two shallow MLPs to model both propagation and transmitter-dependent effects. To further accelerate training and inference speed, we introduce an empty space skipping mechanism to reduce sampling in free space. Experimental results demonstrate that VoxelRF achieves competitive accuracy with significantly reduced computation and limited training data, making it more practical for real-time and resource-constrained wireless channel prediction. © 1997-2012 IEEE.