Admm-Based Fast Algorithm For Robust Multi-Group Multicast Beamforming

We consider robust multi-group multicast beamforming design in massive MIMO large-scale systems. The goal is to minimize the transmit power subject to the minimum signal-to-interference-plus-noise-ratio (SINR) under channel uncertainty. Using the exact worst-case SINR constraint, we transform the problem into a non-convex optimization problem. We develop an alternating direction method of multipliers (ADMM)-based fast algorithm to solve this problem directly with convergence guarantee. Our two-layer ADMM-based algorithm decomposes the non-convex problem into a sequence of convex subproblems, for which we obtain the semi-closed-form or closed-form solutions. Simulation studies show that our algorithm provides a considerable computational advantage over the conventional interior-point method non-convex solver with nearly identical performance.