Geometric Inductive Biases for Semi-Supervised Equalization: The Constellation-Aware Transformer

arXiv:2609.33695 · cs.LG, cs.IT, eess.SP, math.IT · Submitted 2026-09-27 · Read on arXiv

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

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Transformer — 54× in this paper · explained in 2 episodes
• Adaptation — 14× in this paper · explained in 1 episode
• Meta-learning — 11× in this paper · explained in 2 episodes
• Generative Model — 10× in this paper · explained in 2 episodes
• Constellation-Aware Transformer — 9× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Attention Mechanism — 7× in this paper · explained in 3 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Embeddings — 5× in this paper · explained in 2 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Symbol Error Rate — 4× in this paper
• Calibration — 3× in this paper · explained in 7 episodes
• International Conference on Learning Representations — 3× in this paper
• Semi-supervised Learning — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Pseudo-labels — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semi-Supervised Learning (SSL) — 2× in this paper · explained in 2 episodes
• Simple Decision Directed — 2× in this paper
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Composite Loss Function — 1× in this paper · explained in 2 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Network — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Finite Impulse Response — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• IEEE International Symposium on Information Theory — 1× in this paper
• In Advances in Neural Information Processing Systems — 1× in this paper
• In International Conference on Machine Learning — 1× in this paper
• Multi-Layer Perceptron — 1× in this paper
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Signal Processing Advances in Wireless Communications — 1× in this paper
• Signal-to-Noise Ratio — 1× in this paper
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Symmetry Breaking — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Sionna: An Open-Source Library for Next-Generation Physical Layer Research
• Fast Transformer Decoding: One Write-Head is All You Need

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