Learning Linear Filters for Underwater OFDM Channel Estimation with Attention-Aided MMSE

Key Points

AMMSE significantly enhances channel estimation performance across all signal-to-noise ratios, especially in low-SNR conditions.
Simulation results indicate that AMMSE outperforms traditional methods like LS and 1D-MMSE.
Using a transformer model, AMMSE learns a linear MMSE filter that captures important spectral and temporal correlations.
Inference is computationally efficient due to its reliance on matrix-vector multiplication.

Abstract

This letter presents an Attention-aided MMSE (AMMSE) channel estimation method for underwater OFDM systems. To address the severe time variation and multipath effects in underwater acoustic channels, AMMSE leverages a Transformer to learn a linear MMSE filter from data, capturing temporal and spectral correlations. Inference involves only a matrix-vector multiplication, ensuring low complexity. Simulations show that AMMSE outperforms LS, 1D-MMSE, and MMSE across all SNRs, with significant gains in low-SNR conditions.

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Learning Linear Filters for Underwater OFDM Channel Estimation with Attention-Aided MMSE

Key Points

Abstract

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