GeoUQ-GFNet reconstructs dense urban gain radio maps from sparse measurements using geometry priors and uncertainty-guided active sensing, showing consistent gains over non-adaptive sampling on the new UrbanRT-RM ray-tracing benchmark.
Towards Precise Channel Knowledge Map: Exploiting Environmental Information from 2D Visuals to 3D Point Clouds
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The substantial communication resources consumed by conventional pilot-based channel sounding impose an unsustainable overhead, presenting a critical scalability challenge for the future 6G networks characterized by massive channel dimensions, ultra-wide bandwidth, and dense user deployments. As a generalization of radio map, channel knowledge map (CKM) offers a paradigm shift, enabling access to location-tagged channel information without exhaustive measurements. To fully utilize the power of CKM, this work highlights the necessity of leveraging three-dimensional (3D) environmental information, beyond conventional two-dimensional (2D) visual representations, to construct high-precision CKMs. Specifically, we present a novel framework that integrates 3D point clouds into CKM construction through a hybrid model- and data-driven approach, with extensive case studies in real-world scenarios. The experimental results demonstrate the potential for constructing precise CKMs based on 3D environments enhanced with semantic understanding, together with their applications in the next-generation wireless communications. We also release a real-world dataset of measured channel paired with high-resolution 3D environmental data to support future research and validation.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DT-MOO uses a digital twin to jointly optimize coupled objectives in low-altitude communication networks, raising high-quality coverage from 14.0% to 52.9% in 5G real-world tests while delivering net SINR gains.
citing papers explorer
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Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection
GeoUQ-GFNet reconstructs dense urban gain radio maps from sparse measurements using geometry priors and uncertainty-guided active sensing, showing consistent gains over non-adaptive sampling on the new UrbanRT-RM ray-tracing benchmark.
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Building Low-Altitude Communication Networks: A Digital Twin-Based Optimization Framework
DT-MOO uses a digital twin to jointly optimize coupled objectives in low-altitude communication networks, raising high-quality coverage from 14.0% to 52.9% in 5G real-world tests while delivering net SINR gains.