MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction

arXiv:2604.21957 · cs.IT, cs.AI, cs.LG, eess.SP, math.IT · Submitted 2026-04-23 · Read on arXiv

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

Submitted: 2026-04-23

Updated: 2026-08-27

Code: https://github.com/aladinD/MambaCSP

Terminology

Accuracy — 8× in this paper · explained in 1 episode
Embeddings — 4× in this paper · explained in 2 episodes
Transformer — 4× in this paper · explained in 2 episodes
Ablation — 3× in this paper · explained in 1 episode
State Space Models (SSMs) — 3× in this paper · explained in 4 episodes
Unified Framework — 3× in this paper · explained in 5 episodes
Attention Mechanism — 2× in this paper · explained in 2 episodes
Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
Adam — 1× in this paper · explained in 2 episodes
Agentic AI — 1× in this paper · explained in 1 episode
Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Core — 1× in this paper · explained in 2 episodes
Edge AI — 1× in this paper · explained in 2 episodes
Feature Extraction — 1× in this paper · explained in 2 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Gradient Flow — 1× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Pruning — 1× in this paper · explained in 4 episodes
Quantization — 1× in this paper · explained in 12 episodes
Tokenization — 1× in this paper · explained in 2 episodes

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