MambaCSP replaces quadratic-attention LLM backbones with linear-time hybrid SSMs for CSI prediction, delivering 9-12% higher accuracy and up to 3x throughput in MISO-OFDM simulations.
Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback
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AirFM-DDA reparameterizes wireless channel data into the delay-Doppler-angle domain and uses efficient window attention to achieve better zero-shot performance on channel prediction and estimation with lower compute cost.
citing papers explorer
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MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction
MambaCSP replaces quadratic-attention LLM backbones with linear-time hybrid SSMs for CSI prediction, delivering 9-12% higher accuracy and up to 3x throughput in MISO-OFDM simulations.
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AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G
AirFM-DDA reparameterizes wireless channel data into the delay-Doppler-angle domain and uses efficient window attention to achieve better zero-shot performance on channel prediction and estimation with lower compute cost.