WRF-GS: Wireless Radiation Field Reconstruction with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2412.04832v1
• Date: Fri, 06 Dec 2024 07:56:14 GMT
• Title: WRF-GS: Wireless Radiation Field Reconstruction with 3D Gaussian Splatting
• Authors: Chaozheng Wen, Jingwen Tong, Yingdong Hu, Zehong Lin, Jun Zhang,
• Abstract summary: We present WRF-GS, a novel framework for channel modeling based on wireless radiation field (WRF) reconstruction using 3D Gaussian splatting.<n>With a small number of measurements, WRF-GS can synthesize new spatial spectra within milliseconds for a given scene.<n>WRF-GS achieves superior performance in the channel state information prediction task, surpassing existing methods by a significant margin of more than 2.43 dB.
• Score: 8.644949917126755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireless channel modeling plays a pivotal role in designing, analyzing, and optimizing wireless communication systems. Nevertheless, developing an effective channel modeling approach has been a longstanding challenge. This issue has been escalated due to the denser network deployment, larger antenna arrays, and wider bandwidth in 5G and beyond networks. To address this challenge, we put forth WRF-GS, a novel framework for channel modeling based on wireless radiation field (WRF) reconstruction using 3D Gaussian splatting. WRF-GS employs 3D Gaussian primitives and neural networks to capture the interactions between the environment and radio signals, enabling efficient WRF reconstruction and visualization of the propagation characteristics. The reconstructed WRF can then be used to synthesize the spatial spectrum for comprehensive wireless channel characterization. Notably, with a small number of measurements, WRF-GS can synthesize new spatial spectra within milliseconds for a given scene, thereby enabling latency-sensitive applications. Experimental results demonstrate that WRF-GS outperforms existing methods for spatial spectrum synthesis, such as ray tracing and other deep-learning approaches. Moreover, WRF-GS achieves superior performance in the channel state information prediction task, surpassing existing methods by a significant margin of more than 2.43 dB.

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