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.

翻译：与常规估算算法相比,基于深度学习(DL)的系统在性能和复杂性方面显示出巨大的潜力。在本文件中,建议利用频道分布特点的注意机制,通过实现“divide-and-conque”政策,提高高度分解的渠道的准确性,其狭角分布能够提高高度分解的渠道的准确性。具体地说,我们为传统的大型巨型MIMO系统引入了新的关注引导DL渠道估计框架,并设计一种嵌入方法,将关注机制有效地纳入混合模拟-数字结构的完全连接的神经网络。模拟结果表明,在这两种情况下,在以小的复杂间接费用为代价的注意帮助下,频道估计业绩得到显著改善。此外,通过拟议的方法,可以实现不同系统和频道参数的强大稳健度,从而进一步加强其实际价值。我们还调查所学的注意地图的分布情况,以揭示注意的作用,从而以某种程度的可理解性能结束拟议方法。