An ellipsoid-guided selective refinement algorithm improves radio-map fidelity in urban wireless digital twins by prioritizing refinement of a small subset of buildings using only low-fidelity models.
Wireless InSite
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The proposed pretraining framework for safe DRL in CF-MIMO resource management doubles initial energy efficiency, achieves 4.7% higher final EE, maintains 1% delay violation rate, and cuts exploration steps by 50% compared to non-pretrained baselines while matching diffusion model performance at 14x
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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Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins
An ellipsoid-guided selective refinement algorithm improves radio-map fidelity in urban wireless digital twins by prioritizing refinement of a small subset of buildings using only low-fidelity models.
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Generative Learning Enhanced Intelligent Resource Management for Cell-Free Delay Deterministic Communications
The proposed pretraining framework for safe DRL in CF-MIMO resource management doubles initial energy efficiency, achieves 4.7% higher final EE, maintains 1% delay violation rate, and cuts exploration steps by 50% compared to non-pretrained baselines while matching diffusion model performance at 14x
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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.