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

2025-05-31

AI Summary

Existing OFDM channel estimation methods either rely on hard-to-obtain second-order statistics or incur high computational complexity in deep learning–based approaches. To address this, we propose an Attention-enhanced Minimum Mean Square Error (A-MMSE) framework: (i) the first to integrate Transformer-based attention mechanisms into model-driven MMSE filter learning; (ii) a two-stage attention encoder that precisely captures spatiotemporal channel correlations; and (iii) rank-adaptive matrix decomposition enabling flexible trade-offs between estimation accuracy and computational complexity. Crucially, inference requires only a single linear operation, ensuring hardware-friendly deployment. Evaluated under the 3GPP TDL channel model, the proposed method achieves uniformly lower normalized mean square error than state-of-the-art baselines across a wide SNR range, establishing a new Pareto-optimal frontier in performance–complexity trade-off, with over 60% reduction in inference latency.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Improving OFDM channel estimation accuracy with Attention-aided MMSE
• Reducing computational complexity in DNN-based channel estimation methods
• Enhancing learning efficiency via a two-stage Attention encoder

Innovation

• Attention-aided MMSE for linear channel estimation
• Two-stage Attention encoder captures correlation
• Rank-adaptive extension balances complexity and accuracy