Phy2-ExposNet combines physics-informed neural estimation with transformer refinement to map electromagnetic field exposure, cutting error by ~15% and parameters by >80% versus baselines.
Chan- nel knowledge maps for 6g wireless networks: Con- struction, applications, and future challenges
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Phy2-ExposNet: A Physics-Informed Neural Network for EMF Exposure Mapping in Complex Urban Environments
Phy2-ExposNet combines physics-informed neural estimation with transformer refinement to map electromagnetic field exposure, cutting error by ~15% and parameters by >80% versus baselines.
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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.