R-NeRF: Neural Radiance Fields for Modeling RIS-enabled Wireless Environments
<|reference_start|>R-NeRF: Neural Radiance Fields for Modeling RIS-enabled Wireless Environments: Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations caused by various environmental factors and the mobility of RISs. In this paper, we propose a novel modeling approach using Neural Radiance Fields (NeRF) to characterize the dynamics of electromagnetic fields in such environments. Our method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver. This two-stage process accurately characterizes multiple complex transmission paths, enhancing our understanding of signal behavior in real-world scenarios. Our approach predicts the signal field for any specified RIS placement and receiver location, facilitating efficient RIS deployment. Experimental evaluations using both simulated and real-world data validate the significant benefits of our methodology.<|reference_end|>
@article{yang2024r-nerf:,
    title={R-NeRF: Neural Radiance Fields for Modeling RIS-enabled Wireless
  Environments},
    author={Huiying Yang, Zihan Jin, Chenhao Wu, Rujing Xiong, Robert Caiming Qiu,
  Zenan Ling},
    journal={arXiv preprint arXiv:2405.11541},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11541},
    primaryClass={cs.IT eess.SP math.IT}
}