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

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 4440 - 4444
• Main Authors: Mohamadi, Niloofar, Dong, Min, ShahbazPanahi, Shahram
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.06.2021
• Subjects: alternating direction method of multipliers; Array signal processing; Closed-form solutions; computational complexity; Interference; Large-scale systems; Multi-group multicast; Multicast algorithms; robust optimization; Signal processing algorithms; Uncertainty
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9413651

We consider robust multi-group multicast beamforming design in massive multiple-input multiple-output (MIMO) large-scale systems. The goal is to minimize the transmit power subject to the minimum signal-to-interference-plus-noise-ratio (SINR) targets under channel uncertainty. Using the exact worst-case SINR constraints, 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.