Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators

arXiv:2605.08211 · eess.SP, cs.IT, cs.LG, math.IT · Submitted 2026-05-06 · Read on arXiv

eess.SP, cs.IT, cs.LG, math.IT

Submitted: 2026-05-06

Updated: 2026-08-30

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 27× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Meta-learning — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Matrix Completion — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes

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