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.

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，例如线性最小均方误差（LMMSE）估计，通常需要难以在实践中获得的二阶统计量。最近，基于深度神经网络（DNN）的方法被引入以解决此问题；然而，它们往往面临较高的推理复杂度。本文提出了一种基于注意力机制的MMSE（A-MMSE）方法，这是一个基于模型的DNN框架，通过注意力Transformer学习线性MMSE滤波器。一旦训练完成，A-MMSE通过单次线性运算执行信道估计，消除了推理过程中的非线性激活，从而降低了计算复杂度。为了提高A-MMSE的学习效率，我们开发了一个两阶段注意力编码器，用于捕捉OFDM信道的频率和时间相关性结构。我们还引入了一种秩自适应扩展，以实现灵活的性能-复杂度权衡。数值仿真表明，在广泛的信噪比（SNR）条件下，所提出的A-MMSE在归一化MSE方面始终优于其他基线方法。特别是，A-MMSE及其秩自适应扩展提供了更优的性能-复杂度权衡，为实际信道估计提供了一个强大且高效的解决方案。