Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting

• URL: http://arxiv.org/abs/2606.18734v1
• Date: Wed, 17 Jun 2026 06:23:00 GMT
• Title: Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting
• Abstract summary: emphpoint-cloud-assisted tangent splatting (PC-TGS) is first framework to unmeasured outdoor grids.<n>PC-TGS represents environmental scatterers as anisotropic 3D Gaussians.<n>Results highlight potential of PC-TGS to enable geometry-aware and data-efficient channel prediction in large-scale wireless digital twins.
• Score: 27.51525749430508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks. Among various approaches, localized statistical channel modeling (LSCM), which models the channel multipath angular power spectrum (APS) from the reference signal received power (RSRP) measurement, has emerged as a state-of-the-art method tailored for efficient network optimization. However, despite its effectiveness, LSCM cannot predict APS at the vast majority of locations where no measurements are available, which significantly restricts its applicability in large-scale, real-world scenarios. To address this challenge, we present \emph{point-cloud-assisted tangent Gaussian splatting} (PC-TGS), the first framework to \emph{extrapolate} APS to unmeasured outdoor grids by integrating sparse radio measurements with dense LiDAR-based geometry. PC-TGS represents environmental scatterers as anisotropic 3D Gaussians, initialized and refined through a relaxed-mean reparameterization of the raw point cloud. A tangent-plane projection accurately maps each Gaussian into the local angular domain, while a depth-aware electromagnetic splatting process aggregates their contributions. To ensure practical deployment, we derive a closed-form Gaussian-weighted average (GWA) for APS bin integration and provide a provable error bound. { Evaluations on a LiDAR-scanned city-scale dataset (5M points, 6,310 RSRP samples) demonstrate that PC-TGS achieves better APS and RSRP prediction performance compared to state-of-the-art baselines and faster inference time for APS extrapolation task. These results highlight the potential of PC-TGS to enable geometry-aware and data-efficient channel prediction in large-scale wireless digital twins.

Related papers

• Gaussian-JEPA: Joint-Embedding Predictive Learning for 3D Gaussian Splats [91.55220894541151]
  3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance.<n>We introduce Gaussian-JEPA, which predicts representations of held-out Gaussian token blocks from visible context.
  arXiv  Detail & Related papers  (2026-08-16T09:43:50Z)
• Real-Time LiDAR Gaussian Splatting SLAM [2.0524609401792397]
  We present a real-time LiDAR-based framework for Splatting SLAM that tightly couples fast G-ICP registration with sphericalization-based dense mapping for large-scale sequences.<n>We reuse tracking-estimated local covariances to initialize Gaussians with range-aware scales and to derive surface normals for map optimization.<n>On the Newer College dataset, our method achieves an F-score of 86.78% using purely online trajectories at real-time speed.
  arXiv  Detail & Related papers  (2026-07-05T05:55:32Z)
• CuBAS: Information Geometric Curvature-Based Adaptive Sampling for Supervised Classification [52.452902154360565]
  CuBAS is an information-geometric framework for adaptive data selection in supervised classification.<n>A labeled dataset can be viewed as a statistical manifold, on which local curvature, estimated via the ratio of second to first-order observed Fisher information, faithfully encodes the geometric complexity of the data distribution.
  arXiv  Detail & Related papers  (2026-07-03T09:36:02Z)
• Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction [44.36992948504474]
  Wireless radiance field (WRF) reconstruction aims to learn a continuous, queryable representation of radio frequency characteristics over 3D space and direction.<n>We introduce BiSplat-WRF, a planar GS framework that retains the expressiveness of 3D GS while removing unnecessary projections.<n>A bilinear spatial transformer (BST) aggregates inter-primitive relations on an angular grid and, via attention, captures long-range electromagnetic dependencies.
  arXiv  Detail & Related papers  (2026-04-17T12:19:36Z)
• GS^2: Graph-based Spatial Distribution Optimization for Compact 3D Gaussian Splatting [50.2045409672451]
  3D Gaussian Splatting (3DGS) has demonstrated breakthrough performance in novel view synthesis and real-time rendering.<n>It is constrained by the high memory cost due to a huge number of Gaussian points.<n>We propose graph-based spatial distribution optimization for compact 3DGS.
  arXiv  Detail & Related papers  (2026-04-02T10:41:51Z)
• Digital Twin-Assisted Measurement Design and Channel Statistics Prediction [38.5726581069701]
  This work introduces a hybrid channel prediction framework that leverages uncalibrated DTs derived from open-source maps to extract geometry-induced prior information for GP prediction.<n>The proposed method reduces measurement overhead, improves prediction accuracy, and establishes a practical approach for resource-efficient wireless channel prediction.
  arXiv  Detail & Related papers  (2026-03-24T23:43:59Z)
• GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels [18.70495617397063]
  Channel state information (CSI) is essential for adaptive beamforming and robust links in wireless communication systems.<n>We present GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels, the first computation to break the 1 ms latency barrier.
  arXiv  Detail & Related papers  (2025-11-27T22:42:23Z)
• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• Flow-Induced Diagonal Gaussian Processes [7.720921989821054]
  Flow-Induced Diagonal Gaussian Processes (FiD-GP) is a compression framework that incorporates a compact inducing weight matrix.<n>We show how FiD-GP can help to design a single-pass projection for Out-of-Distribution (OoD) detection.
  arXiv  Detail & Related papers  (2025-09-21T16:49:09Z)
• Fluid Antenna System-assisted Physical Layer Secret Key Generation [64.92952968689636]
  This paper investigates physical-layer generation (PLKG) in multiant base station systems by leveraging a fluid antenna system (FAS) to dynamically radio environments.<n>We propose an assisted PLKG model that integrates transmit beamforming and port selection under independent and spatially correlated environments.<n>It is shown that the sliding window-based port selection method introduced in this paper achieves higher KGR with fewer chains through dynamic port selection.
  arXiv  Detail & Related papers  (2025-09-19T03:01:29Z)
• Neural Beam Field for Spatial Beam RSRP Prediction [11.903931127386349]
  Accurately predicting beam-level reference signal received power (RSRP) is essential for beam management in dense wireless networks.<n>This paper proposes Neural Beam Field (NBF), a hybrid neural-physical framework for efficient and interpretable spatial beam RSRP prediction.
  arXiv  Detail & Related papers  (2025-08-09T12:05:51Z)
• GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering [69.67264955234494]
  GeoSplatting is a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling.<n>By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface.<n>This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T17:57:07Z)
• ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining [112.40071212468843]
  3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks.<n>We build a large-scale dataset of 3DGS using the commonly used ShapeNet, ModelNet and averse.<n>We introduce Gaussian-MAE, which highlights the unique benefits of representation learning from Gaussian parameters.
  arXiv  Detail & Related papers  (2024-08-20T14:49:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.