  Accurate and efficient estimation of the high dimensional channels is one of
the critical challenges for practical applications of massive multiple-input
multiple-output (MIMO). In the context of hybrid analog-digital (HAD)
transceivers, channel estimation becomes even more complicated due to
information loss caused by limited radio-frequency chains. The conventional
compressive sensing (CS) algorithms usually suffer from unsatisfactory
performance and high computational complexity. In this paper, we propose a
novel deep learning (DL) based framework for uplink channel estimation in HAD
massive MIMO systems. To better exploit the sparsity structure of channels in
the angular domain, a novel angular space segmentation method is proposed,
where the entire angular space is segmented into many small regions and a
dedicated neural network is trained offline for each region. During online
testing, the most suitable network is selected based on the information from
the global positioning system. Inside each neural network, the region-specific
measurement matrix and channel estimator are jointly optimized, which not only
improves the signal measurement efficiency, but also enhances the channel
estimation capability. Simulation results show that the proposed approach
significantly outperforms the state-of-the-art CS algorithms in terms of
estimation performance and computational complexity.
