  One key feature of massive multiple-input multiple-output systems is the
large number of antennas and users. As a result, reducing the computational
complexity of beamforming design becomes imperative. To this end, the goal of
this paper is to achieve a lower complexity order than that of existing
beamforming methods, via the parallel accelerated random coordinate descent
(ARCD). However, it is known that ARCD is only applicable when the problem is
convex, smooth, and separable. In contrast, the beamforming design problem is
nonconvex, nonsmooth, and nonseparable. Despite these challenges, this paper
shows that it is possible to incorporate ARCD for multicast beamforming by
leveraging majorization minimization and strong duality. Numerical results show
that the proposed method reduces the execution time by one order of magnitude
compared to state-of-the-art methods.
