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


Vol. 51,  No. 1, pp. 121-124, Jan.  2026
10.7840/kics.2026.51.1.121


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  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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[IEEE Style]

T. Ha and J. Park, "Learning Linear Filters for Underwater OFDM Channel Estimation with Attention-Aided MMSE," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 1, pp. 121-124, 2026. DOI: 10.7840/kics.2026.51.1.121.

[ACM Style]

TaeJun Ha and Jeonghun Park. 2026. Learning Linear Filters for Underwater OFDM Channel Estimation with Attention-Aided MMSE. The Journal of Korean Institute of Communications and Information Sciences, 51, 1, (2026), 121-124. DOI: 10.7840/kics.2026.51.1.121.

[KICS Style]

TaeJun Ha and Jeonghun Park, "Learning Linear Filters for Underwater OFDM Channel Estimation with Attention-Aided MMSE," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 1, pp. 121-124, 1. 2026. (https://doi.org/10.7840/kics.2026.51.1.121)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880