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.

翻译：无线信道建模在无线通信系统的设计、分析与优化中起着关键作用。然而，开发有效的信道建模方法一直是一个长期存在的挑战。由于5G及后续网络中更密集的网络部署、更大的天线阵列和更宽的带宽，这一问题变得更为突出。为应对这一挑战，我们提出了WRF-GS，一种基于3D高斯泼溅进行无线辐射场重建的新型信道建模框架。WRF-GS采用3D高斯基元与神经网络来捕捉环境与无线电信号之间的相互作用，从而实现高效的WRF重建和传播特性的可视化。重建后的WRF可用于合成空间频谱，以进行全面的无线信道表征。值得注意的是，仅需少量测量，WRF-GS即可在毫秒级时间内为给定场景合成新的空间频谱，从而支持对时延敏感的应用。实验结果表明，在空间频谱合成任务上，WRF-GS优于射线追踪及其他深度学习方法。此外，WRF-GS在信道状态信息预测任务中取得了卓越的性能，以超过2.43 dB的显著优势超越了现有方法。