  Massive multiple-input-multiple-output (MaMIMO) multicasting has received
significant attention over the last years. MaMIMO is a key enabler of 5G
systems to achieve the extremely demanding data rates of upcoming services.
Multicast in the physical layer is an efficient way of serving multiple users,
simultaneously demanding the same service and sharing radio resources. This
work proposes a subgrouping strategy of multicast users based on their spatial
channel characteristics to improve the channel estimation and precoding
processes. We employ max-min fairness (MMF) power allocation strategy to
maximize the minimum spectral efficiency (SE) of the multicast service.
Additionally, we explore the combination of spatial multiplexing with
orthogonal (time/frequency) multiple access. By varying the number of antennas
at the base station (BS) and users' spatial distribution, we also provide the
optimal subgroup configuration that maximizes the spectral efficiency per
subgroup. Finally, we show that serving the multicast users into two orthogonal
time/frequency intervals offers better performance than only relying on spatial
multiplexing.
