A physics-aware query-conditioned hierarchical graph attention network estimates point-wise transmitter-resolved radio maps from sparse measurements and outperforms baselines on DeepMIMO simulations in direct, residual, and gated regimes.
Analysis of interpolation errors in urban digital surface models created from LIDAR data,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Physics-Aware Query-Conditioned Graph Attention Networks for Radio Map Estimation
A physics-aware query-conditioned hierarchical graph attention network estimates point-wise transmitter-resolved radio maps from sparse measurements and outperforms baselines on DeepMIMO simulations in direct, residual, and gated regimes.