Multi-Group Multicast Beamforming by Superiorized Projections Onto Convex Sets

In this paper, we propose an iterative algorithm to address the nonconvex multi-group multicast beamforming problem with quality-of-service constraints and per-antenna power constraints. We formulate a convex relaxation of as semidefinite program in real Hilbert space, which allows us approximate point feasible set by iteratively applying bounded perturbation resilient fixed-point mapping. Inspired superiorization methodology, use mapping basic algorithm, add each iteration small intent reduce objective value distance rank-constraint sets. prove that sequence perturbations is bounded, so guaranteed converge relaxed program. Simulations show proposed approach outperforms existing algorithms terms both computation time approximation gap many cases.