Construction and Dynamic Update of Channel Gain Maps via 3D Gaussian Splatting

2026-07-23

AI Summary

This work addresses the challenge of efficiently constructing and dynamically updating context-aware, location-dependent channel gain maps (CGMs) in large-scale wireless networks. It proposes a physics-informed approach based on 3D Gaussian Splatting (3DGS), introducing 3DGS for the first time into CGM modeling. The method represents the propagation environment using Gaussian primitives and integrates physical radio propagation mechanisms—including path loss, transmission, and scattering—to generate grid-level channel gains via differentiable rendering. To accommodate dynamic environmental changes, an incremental learning strategy is devised, combining frozen reference primitives with adaptable incremental ones. Experimental results demonstrate that the proposed method achieves high accuracy while significantly reducing computational overhead, enabling real-time updates and fine-grained representation of CGMs.

Abstract

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.

Problem

• Channel Gain Maps
• 3D Gaussian Splatting
• Dynamic Update
• Wireless Network
• Propagation Modeling

Innovation

• 3D Gaussian Splatting
• Channel Gain Maps
• Physics-informed Modeling
• Incremental Learning
• Differentiable Rendering