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

TL;DR

This paper introduces A-MMSE, a neural network framework using attention mechanisms to learn linear MMSE filters for OFDM channel estimation, achieving high accuracy with reduced complexity.

Contribution

It proposes a novel attention-based DNN method that learns linear MMSE filters, improving efficiency and performance in OFDM channel estimation.

Findings

Outperforms baseline methods in normalized MSE across SNRs

Reduces inference complexity by performing a single linear operation

Provides a flexible performance-complexity trade-off with rank adaptation

Abstract

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.