  Massive MIMO is currently a leading physical layer technology candidate that
can dramatically enhance throughput in 5G systems, for both unicast and
multicast transmission modalities. As antenna elements are becoming smaller and
cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial
to perform antenna selection at the transmitter, such that the available RF
chains are switched to an appropriate subset of antennas. This paper considers
the joint problem of multicast beamforming and antenna selection for a single
multicast group in massive MIMO systems. The prior state-of-art for this
problem relies on semi-definite relaxation (SDR), which cannot scale up to the
massive MIMO regime. A successive convex approximation (SCA) based approach is
proposed to tackle max-min fair joint multicast beamforming and antenna
selection. The key idea of SCA is to successively approximate the non-convex
problem by a class of non-smooth, convex optimization problems. Two fast and
memory efficient first-order methods are proposed to solve each SCA subproblem.
Simulations demonstrate that the proposed algorithms outperform the existing
state-of-art approach in terms of solution quality and run time, in both
traditional and especially in massive MIMO settings.
