Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) proble...

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Vydáno v:IEEE transactions on signal processing Ročník 70; s. 2046 - 2061
Hlavní autoři: Mohamadi, Niloofar, Dong, Min, ShahbazPanahi, Shahram
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
alternating direction method of multipliers (ADMM)
Array signal processing
Beamforming
Closed form solutions
Complexity
Convergence
Covariance matrix
Exact solutions
Interference
Iterative algorithms
Iterative methods
large-scale systems
low-complexity
Multicast algorithms
Multicasting
Optimization
Optimization techniques
Quality of service
Robust multicast beamforming
Robustness
Signal processing algorithms
Signal to noise ratio
ISSN:1053-587X, 1941-0476
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Shrnutí:We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2022.3160004