  Hybrid precoding design can be challenging for broadband millimeter-wave
(mmWave) massive MIMO due to the frequency-flat analog precoder in radio
frequency (RF). Prior broadband hybrid precoding work usually focuses on
fully-connected array (FCA), while seldom considers the energy-efficient
partially-connected subarray (PCS) including the fixed subarray (FS) and
dynamic subarray (DS). Against this background, this paper proposes a machine
learning based broadband hybrid precoding for mmWave massive MIMO with DS.
Specifically, we first propose an optimal hybrid precoder based on principal
component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for
each subarray is extracted from the principle component of the optimal
frequency-selective precoders for fully-digital MIMO. Moreover, we extend the
PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical
clustering (AHC) algorithm developed from machine learning is proposed to group
the DS for improved spectral efficiency (SE). Finally, we investigate the
energy efficiency (EE) of the proposed scheme for both passive and active
antennas. Simulations have confirmed that the proposed scheme outperforms
conventional schemes in both SE and EE.
