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-...
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| Veröffentlicht in: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 4440 - 4444 |
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| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
06.06.2021
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| Schlagworte: | |
| ISSN: | 2379-190X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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. |
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| ISSN: | 2379-190X |
| DOI: | 10.1109/ICASSP39728.2021.9413651 |