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

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-...

Full description

Saved in:
Bibliographic Details
Published inProceedings 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
LanguageEnglish
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
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9413651