State Space Models for Wireless Channel Prediction
Dr. Lukas Mauch, Prof. Hans Schotten
RPTU Kaiserslautern / 6G Research Hub
Abstract
We apply structured state space models (S4/Mamba) to wireless channel prediction, demonstrating that these architectures offer superior long-range sequence modeling compared to transformers and LSTMs for time-varying channels. Our S4-Channel model predicts channel state information 10-50ms into the future with 40% lower prediction error than transformer baselines, while requiring linear rather than quadratic computation in sequence length. This enables predictive beamforming and proactive resource allocation.
AI Summary
- Applies S4/Mamba state space models to wireless channel prediction.
- 40% lower prediction error than transformer baselines for 10-50ms lookahead.
- Linear computation complexity enables longer prediction horizons.
- Enables predictive beamforming and proactive resource allocation in 6G.
Key Findings
- 1State space models capture channel dynamics more efficiently than attention mechanisms.
- 2Prediction accuracy degrades gracefully with increasing lookahead horizon.
- 3Model transfers across frequency bands with minimal fine-tuning.
Industry Implications
Opens a new direction for temporal modeling in wireless communications.
Enables proactive network management rather than reactive optimization.
Could reduce handover failures in high-mobility 6G scenarios.
Read the Original Paper
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