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 inference 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 signal-to-noise ratio (SNR) conditions. In particular, the A-MMSE and its rank-adaptive extension establish a new frontier in the performance-complexity trade-off, providing a powerful yet highly efficient solution for practical channel estimation

翻译：在正交频分复用（OFDM）系统中，精确的信道估计至关重要。经典的基于信号处理的方法，如最小均方误差（MMSE）估计，通常需要难以在实际中获取的二阶统计量。近期提出的基于深度神经网络的方法旨在解决此问题，但其推断复杂度往往较高。本文提出了一种基于注意力机制的MMSE（A-MMSE）方法，这是一种新颖的基于模型的深度神经网络框架，它通过注意力Transformer学习最优的MMSE滤波器。一旦训练完成，A-MMSE仅通过单次线性运算即可完成信道估计，在推断过程中消除了非线性激活，从而降低了计算复杂度。为了提升A-MMSE的学习效率，我们设计了一种两阶段注意力编码器，旨在有效捕捉信道相关结构。此外，所提出的A-MMSE的秩自适应扩展版本允许在复杂度与信道估计精度之间进行灵活权衡。基于3GPP TDL信道模型的大量仿真表明，在广泛的信噪比（SNR）条件下，所提出的A-MMSE在归一化均方误差方面持续优于其他基线方法。特别是，A-MMSE及其秩自适应扩展版本在性能-复杂度权衡方面确立了新的前沿，为实际信道估计提供了一个强大且高效的解决方案。