Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition

arXiv:2602.04083 · eess.SP, cs.AI · Submitted 2026-08-19 · Read on arXiv

Alexandre Barbosa de Lima

eess.SP, cs.AI

Submitted: 2026-08-19

Updated: 2026-08-20

Code: https://github.com/alexandreblima/MIMO-TN

Terminology

• Tucker Decomposition — 11× in this paper · explained in 2 episodes
• Core — 9× in this paper · explained in 2 episodes
• Sample Complexity — 8× in this paper · explained in 15 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Inverse Problem — 6× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Monte Carlo — 4× in this paper
• Regularization — 4× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Matrix Completion — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Steering — 2× in this paper · explained in 2 episodes
• U-net architecture — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Physics-Informed Generative Modeling of Wireless Channels
• Adam: A Method for Stochastic Optimization
• DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

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