  Hybrid precoding has been recognized as a promising technology to combat the
path loss of millimeter wave signals in massive multiple-input multiple-output
(MIMO) systems. However, due to the joint optimization of the digital and
analog precoding matrices as well as extra constraints for the analog part, the
hybrid precoding design is still a tough issue in current research. In this
paper, we adopt the thought of clustering in unsupervised learning and provide
design schemes for fully-connected hybrid precoding (FHP) and
adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems.
For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based)
scheme to explore the relevance among RF chains in optimal hybrid procoding
design. The similar RF chains are merged into an individual RF chain when
insufficient RF chains are equipped at the base station (BS). For AHP, we
propose the modified-K-means-based (MKM-based) scheme to explore the relevance
among antennas at the BS. The similar antennas are supported by the same RF
chain to make full use of the flexible connection in AHP. Particularly, in
proposed MKM-based AHP design, the clustering centers are updated by
alternating-optimum-based (AO-based) scheme with a special initialization
method, which is capable to individually provide feasible sub-connected hybrid
precoding (SHP) design. Simulation results highlight the superior spectrum
efficiency of proposed HAC-based FHP scheme, and the high power efficiency of
proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified
to effectively handle the inter-user interference and outperform the existing
work.
