用于无线辐射场重建的神经表征：一种3D高斯泼溅方法
Neural Representation for Wireless Radiation Field Reconstruction: A 3D Gaussian Splatting Approach
AI总结：
针对下一代网络中无线信道建模的难题，提出基于3D高斯泼溅的WRF-GS及融合电磁波物理的增强版WRF-GS+框架，实现无线辐射场高效重建与信道精准表征，性能优于现有方法。
摘要：
无线信道建模在无线通信系统的设计、分析与优化中发挥着核心作用。然而，开发有效的信道建模方法长期以来一直是一项难题。随着下一代网络中网络部署更密集、天线阵列规模更大、带宽更宽，这一问题进一步加剧。为应对这一挑战，我们提出了WRF-GS，这是一种基于3D高斯泼溅（3D-GS）进行无线辐射场（WRF）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，能够实现高效的WRF重建与传播特性可视化。重建后的WRF可用于合成空间谱，以实现全面的无线信道表征。尽管WRF-GS展现出了显著的有效性，但它在捕捉由复杂多径效应导致的高频信号变化方面存在局限。为克服这些局限，我们提出了WRF-GS+，这是一种将电磁波物理特性融入神经网络设计的增强型框架。WRF-GS+利用可变形3D高斯对WRF的静态和动态分量进行建模，显著提升了其表征信号变化的能力。此外，WRF-GS+通过简化3D-GS建模流程并提升计算效率，优化了泼溅过程。实验结果表明，WRF-GS和WRF-GS+在空间谱合成任务上的表现均优于基线方法，包括射线追踪法和其他深度学习方法。值得注意的是，WRF-GS+在接收信号强度指示（RSSI）和信道状态信息（CSI）预测任务上达到了当前最优性能，分别较现有方法提升超过0.7 dB和3.36 dB。
英文摘要：
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 long-standing challenge. This issue has been escalated due to denser network deployment, larger antenna arrays, and broader bandwidth in next-generation 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 (3D-GS). 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. While WRF-GS demonstrates remarkable effectiveness, it faces limitations in capturing high-frequency signal variations caused by complex multipath effects. To overcome these limitations, we propose WRF-GS+, an enhanced framework that integrates electromagnetic wave physics into the neural network design. WRF-GS+ leverages deformable 3D Gaussians to model both static and dynamic components of the WRF, significantly improving its ability to characterize signal variations. In addition, WRF-GS+ enhances the splatting process by simplifying the 3D-GS modeling process and improving computational efficiency. Experimental results demonstrate that both WRF-GS and WRF-GS+ outperform baselines for spatial spectrum synthesis, including ray tracing and other deep-learning approaches. Notably, WRF-GS+ achieves state-of-the-art performance in the received signal strength indication (RSSI) and channel state information (CSI) prediction tasks, surpassing existing methods by more than 0.7 dB and 3.36 dB, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
