Attention-Aided MMSE for OFDM Channel Estimation: Learning Linear Filters with Attention
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917224189526016 |
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| author | Ha, TaeJun Jung, Chaehyun Kim, Hyeonuk Park, Jeongwoo Park, Jeonghun |
| author_facet | Ha, TaeJun Jung, Chaehyun Kim, Hyeonuk Park, Jeongwoo Park, Jeonghun |
| contents | In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing-based approaches, such as linear minimum mean-squared error (LMMSE) estimation, often require second-order statistics that are difficult to obtain in practice. Recent deep neural network (DNN)-based methods have been introduced to address this; yet they often suffer from high inference complexity. This paper proposes an Attention-aided MMSE (A-MMSE), a model-based DNN framework that learns the linear MMSE filter via the Attention Transformer. Once trained, the A-MMSE performs channel estimation through a single linear operation, eliminating nonlinear activations during inference and thus reducing computational complexity. To improve the learning efficiency of the A-MMSE, we develop a two-stage Attention encoder that captures the frequency and temporal correlation structure of OFDM channels. We also introduce a rank-adaptive extension that enables a flexible performance-complexity trade-off. Numerical simulations show that the proposed A-MMSE consistently outperforms other baseline methods in terms of normalized MSE across a wide range of signal-to-noise ratio (SNR) conditions. In particular, the A-MMSE and its rank-adaptive extension provide an improved performance-complexity trade-off, providing a powerful and highly efficient solution for practical channel estimation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_00452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attention-Aided MMSE for OFDM Channel Estimation: Learning Linear Filters with Attention Ha, TaeJun Jung, Chaehyun Kim, Hyeonuk Park, Jeongwoo Park, Jeonghun Signal Processing Artificial Intelligence Machine Learning In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing-based approaches, such as linear minimum mean-squared error (LMMSE) estimation, often require second-order statistics that are difficult to obtain in practice. Recent deep neural network (DNN)-based methods have been introduced to address this; yet they often suffer from high inference complexity. This paper proposes an Attention-aided MMSE (A-MMSE), a model-based DNN framework that learns the linear MMSE filter via the Attention Transformer. Once trained, the A-MMSE performs channel estimation through a single linear operation, eliminating nonlinear activations during inference and thus reducing computational complexity. To improve the learning efficiency of the A-MMSE, we develop a two-stage Attention encoder that captures the frequency and temporal correlation structure of OFDM channels. We also introduce a rank-adaptive extension that enables a flexible performance-complexity trade-off. Numerical simulations show that the proposed A-MMSE consistently outperforms other baseline methods in terms of normalized MSE across a wide range of signal-to-noise ratio (SNR) conditions. In particular, the A-MMSE and its rank-adaptive extension provide an improved performance-complexity trade-off, providing a powerful and highly efficient solution for practical channel estimation. |
| title | Attention-Aided MMSE for OFDM Channel Estimation: Learning Linear Filters with Attention |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.00452 |