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Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning-based designs have exhibited great potential in terms of both performance and complexity. In this paper, an attention-aided deep neural network is proposed for channel estimation in hybrid analog-digital massive MIMO systems. Specifically, the integrated attention mechanism automatically realizes the “divide-and-conquer” policy to exploit the distribution characteristics of highly separable channels with narrow angular spread. Simulation results show that the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead, and the strong robustness further strengthens the practical value of the proposed approach. Moreover, the distributions of learned attention maps are also …