AI RESEARCH
MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction
arXiv CS.AI
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ArXi:2604.21957v1 Announce Type: cross Recent works have nstrated that attention-based transformer and large language model (LLM) architectures can achieve strong channel state prediction (CSP) performance by capturing long-range temporal dependencies across channel state information (CSI) sequences. However, these models suffer from quadratic scaling in sequence length, leading to substantial computational cost, memory consumption, and inference latency, which limits their applicability in real-time and resource-constrained wireless deployments.