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.
DeepMIMO: A generic deep learning dataset for mil- limeter wave and massive MIMO applications
2 Pith papers cite this work. Polarity classification is still indexing.
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Cost-aware full-model fine-tuning with joint entropy coding and structured sparsity prior improves rate-distortion performance of neural CSI compression under distribution shifts.
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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.
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Neural CSI Compression Fine-Tuning: Taming the Communication Cost of Model Updates
Cost-aware full-model fine-tuning with joint entropy coding and structured sparsity prior improves rate-distortion performance of neural CSI compression under distribution shifts.