This paper investigates the construction of channel knowledge map (CKM) from sparse channel measurements. Dif ferent from conventional two-/three-dimensional (2D/3D) CKM approaches assuming fixed base station configurations, we present a six-dimensional (6D) CKM framework named bidirectional wireless Gaussian splatting (BiWGS), which is capable of mod eling wireless channels across dynamic transmitter (Tx) and receiver (Rx) positions in 3D space. BiWGS uses Gaussian el lipsoids to represent virtual scatterer clusters and environmental obstacles in the wireless environment. By properly learning the bidirectional scattering patterns and complex attenuation profiles based on channel measurements, these ellipsoids inherently cap ture the electromagnetic transmission characteristics of wireless environments, thereby accurately modeling signal transmission under varying transceiver configurations. Experiment results show that BiWGS significantly outperforms classic multi-layer perception (MLP) for the construction of 6D channel power gain map with varying Tx-Rx positions, and achieves spatial spectrum prediction accuracy comparable to the state-of-the art wireless radiation field Gaussian splatting (WRF-GS) for 3D CKM construction. This validates the capability of the proposed BiWGS in accomplishing dimensional expansion of 6D CKM construction, without compromising fidelity.
XFreq-GS: Cross-Frequency Wireless Radiation Field Reconstruction with 3D Gaussian Splatting
School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Dept. of ECE, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）
Octree-Context Gaussian Splatting with Multi-Order Propagation for Channel Knowledge Maps
Heisenberg Research Center, Huawei Technologies Duesseldorf GmbH（海森堡研究中心，华为技术杜塞尔多夫有限公司）；Department of Electrical Engineering and Computer Science, Technical University of Berlin（电气与计算机工程系，柏林工业大学）
Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction
Munich Research Center, Huawei Technologies Duesseldorf GmbH, Munich, Germany Department of Electrical Engineering（慕尼黑研究中心，华为技术杜塞尔多夫有限公司，慕尼黑，德国电气工程系）；Computer Science, Technical University of Berlin, Berlin, Germany（计算机科学，柏林技术大学，柏林，德国）
RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat Sen University（中山大学深圳校区智能系统工程学院）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）大数据研究院）；School of Artificial Intelligence, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）人工智能学院）；Networking and User Experience Laboratory, Huawei Technologies（华为技术有限公司网络与用户体验实验室）
Green Robotic Mixed Reality with Gaussian Splatting
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of IOTSC, Department of Computer and Information Science, University of Macau（物联网科学与技术国家重点实验室，澳门大学计算机与信息科学学院）；Peng Cheng Laboratory（鹏城实验室）；Manifold Tech Limited（曼彻斯特科技有限公司）；School of Electrical Engineering and Telecommunications, the University of New South Wales（新南威尔士大学电子与通信工程学院）
PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting
Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA（电气与计算机工程系，南方 Methodist 大学，德克萨斯州达拉斯市）；Department of Engineering Technology, University of Houston, Houston, TX, USA（工程技术系，德克萨斯大学休斯敦分校）
A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness
Xidian University（西安电子科技大学）；The Hong Kong University of Science and Technology（香港科技大学）；Technical University of Berlin（柏林工业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
