MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction
Recent works have demonstrated 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. In this paper, we investigate whether selective state space models (SSMs) can serve as a hardware-efficient alternative for CSI prediction. We propose MambaCSP, a hybrid-attention...
| Authors | Aladin Djuhera, Haris Gacanin, Holger Boche |
|---|---|
| Primary category | cs.IT |
| Published | 23 April 2026 |
| Last updated at source | 27 August 2026 |
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