# Massive MIMO Multicast Beamforming Via Accelerated Random Coordinate Descent

## TL;DR

This paper introduces a novel approach using accelerated random coordinate descent combined with majorization minimization to efficiently solve the complex nonconvex multicast beamforming problem in massive MIMO systems, significantly reducing computation time.

## Contribution

It presents a new method that adapts ARCD for nonconvex, nonsmooth, and nonseparable problems in massive MIMO beamforming, achieving lower computational complexity.

## Key findings

- Reduces execution time by an order of magnitude.
- Successfully applies ARCD to a nonconvex problem.
- Outperforms state-of-the-art methods in efficiency.

## Abstract

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.