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

• URL: http://arxiv.org/abs/2506.00452v1
• Date: Sat, 31 May 2025 08:12:04 GMT
• Title: Attention-Aided MMSE for OFDM Channel Estimation: Learning Linear Filters with Attention
• Authors: TaeJun Ha, Chaehyun Jung, Hyeonuk Kim, Jeongwoo Park, Jeonghun Park,
• Abstract summary: We propose an Attention-aided MMSE that learns the optimal MMSE filter via the Attention Transformer.<n>The A-MMSE estimates the channel through a single linear operation for channel estimation, eliminating nonlinear activations during inference.<n>A rank-adaptive extension of the proposed A-MMSE allows flexible trade-offs between complexity and channel estimation accuracy.
• Score: 9.919010430380476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing based approaches, such as minimum mean-squared error (MMSE) estimation, often require second-order statistics that are difficult to obtain in practice. Recent deep neural networks based methods have been introduced to address this; yet they often suffer from high complexity. This paper proposes an Attention-aided MMSE (A-MMSE), a novel model-based DNN framework that learns the optimal MMSE filter via the Attention Transformer. Once trained, the A-MMSE estimates the channel through a single linear operation for channel estimation, eliminating nonlinear activations during inference and thus reducing computational complexity. To enhance the learning efficiency of the A-MMSE, we develop a two-stage Attention encoder, designed to effectively capture the channel correlation structure. Additionally, a rank-adaptive extension of the proposed A-MMSE allows flexible trade-offs between complexity and channel estimation accuracy. Extensive simulations with 3GPP TDL channel models demonstrate that the proposed A-MMSE consistently outperforms other baseline methods in terms of normalized MSE across a wide range of SNR conditions. In particular, the A-MMSE and its rank-adaptive extension establish a new frontier in the performance complexity trade-off, redefining the standard for practical channel estimation methods.

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