  Hybrid beamforming is a promising technique to reduce the complexity and cost
of massive multiple-input multiple-output (MIMO) systems while providing high
data rate. However, the hybrid precoder design is a challenging task requiring
channel state information (CSI) feedback and solving a complex optimization
problem. This paper proposes a novel RSSI-based unsupervised deep learning
method to design the hybrid beamforming in massive MIMO systems. Furthermore,
we propose i) a method to design the synchronization signal (SS) in initial
access (IA); and ii) a method to design the codebook for the analog precoder.
We also evaluate the system performance through a realistic channel model in
various scenarios. We show that the proposed method not only greatly increases
the spectral efficiency especially in frequency-division duplex (FDD)
communication by using partial CSI feedback, but also has near-optimal sum-rate
and outperforms other state-of-the-art full-CSI solutions.
