Multi-Group Multicast Beamforming: Optimal Structure and Efficient Algorithms

Dong, Min; Wang, Qiqi

doi:10.1109/tsp.2020.2994753

Cited by 31 publications

(69 citation statements)

References 47 publications

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“…In the literature, numerical optimization methods have been devoted to finding suboptimal solutions. Recently, the optimal solution structure to this multicast beamforming QoS problem has been obtained in [21]. Using this structure, problem Po is transformed into an equivalent problem of a much lower dimension to obtain the solution with a very low computational complexity that does not grow with the number of antennas.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…It is shown by [21,Theorem 1] that the optimal solution to Po is a weighted MMSE beamformer given by…”

Section: Optimal Multicast Beamforming Structurementioning

confidence: 99%

“…Recently, the optimal multicast beamforming structure has been obtained in [21]. It is shown that the optimal solution is a weighted MMSE filter with an inherent low-dimensional structure.…”

Section: Introductionmentioning

confidence: 99%

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First-Order Fast Algorithm for Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems

Zhang

Dong

Liang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider multi-group multicast beamforming in large-scale systems to minimize the transmit power subject to the signal-tointerference-plus-noise ratio (SINR) requirements. Based on the optimal multicast beamforming structure, we propose a fast first-order algorithm to obtain the beamforming solution. The algorithm utilizes the successive convex approximation (SCA) method and solves each SCA subproblem by dual reformulation along with the extragradient method for fast closed-form updates. Initialization methods are also explored, including an extragradient-based fast initialization approach that is proposed to generate initial feasible points for SCA. Simulations show that the proposed algorithm provides a near-optimal performance with substantially lower computational complexity for large-scale systems than the existing algorithm.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

“…It is shown by [21,Theorem 1] that the optimal solution to Po is a weighted MMSE beamformer given by…”

Section: Optimal Multicast Beamforming Structurementioning

confidence: 99%

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First-Order Fast Algorithm for Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems

Zhang

Dong

Liang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Note that WD ik W H = w i w H i − γ ik l∈G −i w l w H l . Using the solution in (6), we obtain the minimum objective in (5). Substituting the expression of the minimum objective into (4), we transform Po into the following equivalent problem…”

Section: Reformulation Via Exact Worst-case Sinrmentioning

confidence: 99%

“…Multicast beamforming design has been studied by many in the literature for a single-user group [1,3], multiple groups [4][5][6][7], multicell networks [8][9][10][11], and relay networks [12], assuming perfect channel state information (CSI). Due to the NP-hard nature of the multicasting problems, numerical optimization methods or signal processing techniques have been sought to find good suboptimal solutions.…”

Section: Introductionmentioning

confidence: 99%

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

Mohamadi

Dong

Shahbazpanahi

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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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