  Channel estimation is one of the key issues in practical massive
multiple-input multiple-output (MIMO) systems. Compared with conventional
estimation algorithms, deep learning (DL) based ones have exhibited great
potential in terms of performance and complexity. In this paper, an attention
mechanism, exploiting the channel distribution characteristics, is proposed to
improve the estimation accuracy of highly separable channels with narrow
angular spread by realizing the "divide-and-conquer" policy. Specifically, we
introduce a novel attention-aided DL channel estimation framework for
conventional massive MIMO systems and devise an embedding method to effectively
integrate the attention mechanism into the fully connected neural network for
the hybrid analog-digital (HAD) architecture. Simulation results show that in
both scenarios, the channel estimation performance is significantly improved
with the aid of attention at the cost of small complexity overhead.
Furthermore, strong robustness under different system and channel parameters
can be achieved by the proposed approach, which further strengthens its
practical value. We also investigate the distributions of learned attention
maps to reveal the role of attention, which endows the proposed approach with a
certain degree of interpretability.
