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.
Point cloud environment-based channel knowledge map construction
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UNVERDICTED 4representative 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.
A Transformer-based generative model builds an environment-aware channel knowledge base that is injected into JSCC encoders and decoders, achieving 10^{-3} level channel estimation error and outperforming benchmarks in semantic communication performance.
The work shows that 3D point clouds with semantic labels enable more precise location-tagged channel predictions than 2D visuals in measured real-world settings and releases a paired dataset.
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.
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Generative Channel Knowledge Base With Environmental Information for Joint Source-Channel Coding in Semantic Communications
A Transformer-based generative model builds an environment-aware channel knowledge base that is injected into JSCC encoders and decoders, achieving 10^{-3} level channel estimation error and outperforming benchmarks in semantic communication performance.
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Towards Precise Channel Knowledge Map: Exploiting Environmental Information from 2D Visuals to 3D Point Clouds
The work shows that 3D point clouds with semantic labels enable more precise location-tagged channel predictions than 2D visuals in measured real-world settings and releases a paired dataset.