AI 中文总结
研究基于三维高斯平铺为大规模网络构建及动态更新基于网格的信道增益图，提出基于物理知识的高斯平铺信道增益模型及增量学习机制，实现准确重建、适应动态变化并达成精度-复杂度良好权衡。
AI 中文摘要
信道知识图（CKM）已成为一种有前景的技术，可为无线网络设计提供特定场景和位置相关的传播知识。本文研究使用三维高斯平铺（3DGS）为大规模网络构建和动态更新一种特定类型的CKM，即基于网格的信道增益图（CGM）。首先制定基于网格的信道增益模型，抑制相位敏感的小尺度波动。在此基础上开发基于物理知识的高斯平铺信道增益（GS-CG）模型。为适应实时环境变化，进一步提出增量学习机制。实验结果表明，所提GS-CG方法能准确重建基于网格的CGM，有效适应动态环境变化，在快速CGM细化方面实现了良好的精度-复杂度权衡。
英文摘要
Channel knowledge maps (CKMs) have emerged as a promising technique for providing scene-specific and location-dependent propagation knowledge to enable environment-aware wireless network design. This paper investigates the construction and dynamic updating of a particular type of CKM, namely grid-based channel gain maps (CGMs), for large-scale networks using three-dimensional Gaussian splatting (3DGS). First, we formulate a grid-based channel gain model, where each map entry is defined as the locally averaged channel gain over a receiver grid, thereby suppressing phase-sensitive small-scale fluctuations. The resulting channel gain is decomposed into distance-dependent attenuation, path transmittance, and effective scattering contributions. Based on this decomposition, we develop a physics-informed Gaussian-splatting-based channel gain (GS-CG) model, which represents the propagation environment as a set of Gaussian primitives. The proposed model maps Gaussian geometry, opacity, and directional features to propagation-related factors and renders grid-level channel gains through a differentiable process. To accommodate real-time environmental changes, we further propose an incremental learning mechanism that updates a static reference GS-CG representation into a dynamic CGM. Specifically, the reference Gaussian primitives are frozen, while a compact set of tunable Gaussians is introduced to capture newly induced local channel-gain variations from sparse measurements.Numerical results demonstrate that the proposed GS-CG methods accurately reconstruct grid-based CGMs, efficiently adapt to dynamic environmental changes, and achieve a favorable accuracy-complexity tradeoff for fast CGM refinement.
