NeRF-APT: A New NeRF Framework for Wireless Channel Prediction

Neural radiance fields (NeRFs) have recently attracted significant attention in the field of wireless channel prediction, primarily due to their capability for high-fidelity reconstruction of complex wireless measurement environments. However, the ray-tracing component of NeRF-based methods faces challenges in realistically representing wireless scenarios, mainly due to the limited expressive power of multilayer perceptrons (MLPs). To overcome this issue, in this paper, we propose NeRF-APT, an encoder-decoder architecture integrated within a NeRF-based wireless channel prediction framework. Our architecture leverages the strengths of NeRF-like models in learning environmental features and exploits encoder-decoder modules' capabilities for critical information extraction. Additionally, we incorporate an attention mechanism within the skip connections between encoder and decoder layers, significantly enhancing contextual understanding across layers. Extensive experimental evaluations conducted on several realistic and synthetic datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches in wireless channel prediction.