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

Zhang, Chong; Dong, Min; Liang, Ben

doi:10.1109/icassp39728.2021.9414396

Cited by 6 publications

(10 citation statements)

References 26 publications

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“…to obtain the weights and therefore the optimal solution of the multi-group multicast beamforming problem. This optimal structure has been used by other studies to develop fast first-order algorithm for multi-group multicast beamforming with the QoS [43] and the MMF problem formulation [44].…”

Section: Multicast Beamformingmentioning

confidence: 99%

“…ADMM [74] is a robust and fast numerical method for solving large-scale problems. It has been widely considered in recent years in many applications [23,[75][76][77][78][79][80].…”

Section: Challenges Of Massive Mimomentioning

confidence: 99%

“…The ADMM method has been increasingly attracting attention in the beamforming design problems for large-scale systems, due to it's benefits in reducing the problem size as well as computational complexity. This method has been studied in different works to develop a fast algorithm for multi-group multicast beamforming problem under a perfect channel state information (CSI) [23,80] or the robust design [78,79].…”

Section: Challenges Of Massive Mimomentioning

confidence: 99%

“…2) MBF-SCA [27]: Computing unicast and multicast weight vectors by SCA, where the interior-point method is used in each SCA iteration. 3) MBF-ADMM [80]:…”

Section: Performance Comparisonmentioning

confidence: 99%

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Fast Algorithm for Joint Unicast and Multicast Beamforming in Large-Scale Systems

Mohammadi

Dong

Shahbazpanahi

2021

2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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We consider a joint unicast and multi-group multicast beamforming problem for massive multiple-input multiple-output (MIMO) system with a large number of unicast users. We propose an alternating direction method of multipliers (ADMM)-based fast algorithm that efficiently obtains the beamforming solutions for unicast and multicast users to minimize the transmit power subject to quality-of-service constraints.Utilizing the optimal multicast beamforming structure obtained recently, we separate the original problem into unicast and multicast subproblems to be solved using the alternating optimization technique. We solved the unicast subproblem in closed-form by exploring the unicast beamforming structure, which reduces the computational complexity, substantially. For the multicast subproblem, we apply the successive convex approximation (SCA) method to solve it iteratively. Each SCA subproblem is then reformulated to the ADMM form, providing the closed-form update for the multicast subproblem. Simulation results show that the proposed algorithm achieves a near-optimal performance with low complexity for large-scale systems.

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Section: Multicast Beamformingmentioning

confidence: 99%

“…ADMM [74] is a robust and fast numerical method for solving large-scale problems. It has been widely considered in recent years in many applications [23,[75][76][77][78][79][80].…”

Section: Challenges Of Massive Mimomentioning

confidence: 99%

Section: Challenges Of Massive Mimomentioning

confidence: 99%

“…2) MBF-SCA [27]: Computing unicast and multicast weight vectors by SCA, where the interior-point method is used in each SCA iteration. 3) MBF-ADMM [80]:…”

Section: Performance Comparisonmentioning

confidence: 99%

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Fast Algorithm for Joint Unicast and Multicast Beamforming in Large-Scale Systems

Mohammadi

Dong

Shahbazpanahi

2021

2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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“…Min Dong is with the Department of Electrical, Computer and Software Engineering, Ontario Tech University, Canada (e-mail: min.dong@ontariotechu.ca). Part of this work was presented in [1]. problem sizes, SDR provides a good approximate solution [4]- [6], [8].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low-Complexity Algorithms with Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems

Zhang¹,

Dong²,

Liang³

2022

Preprint

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We consider an ultra-low-complexity multi-group multicast beamforming design for large-scale systems. For the quality-of-service (QoS) problem, by utilizing the optimal multicast beamforming structure obtained recently in [2], we convert the original problem into a non-convex weight optimization problem of a lower dimension and propose two fast first-order algorithms to solve it. Both algorithms are based on successive convex approximation (SCA) and provide fast iterative updates to solve each SCA subproblem. The first algorithm uses a saddle point reformulation in the dual domain and applies the extragradient method with an adaptive step-size procedure to find the saddle point with simple closed-form updates. The second algorithm adopts the alternating direction method of multipliers (ADMM) method by converting each SCA subproblem into a favorable ADMM structure. The structure leads to simple closed-form ADMM updates, where the problem in each update block can be further decomposed into parallel subproblems of small sizes, for which closed-form solutions are obtained. We also propose efficient initialization methods to obtain favorable initial points that facilitate fast convergence. Furthermore, taking advantage of the proposed fast algorithms, for the max-min fair (MMF) problem, we propose a simple closed-form scaling scheme that directly uses the solution obtained from the QoS problem, avoiding the conventional computationally expensive method that iteratively solves the inverse QoS problem. We further develop lower and upper bounds on the performance of this scaling scheme. Simulation results show that the proposed algorithms offer near-optimal performance with substantially lower computational complexity than the state-of-the-art algorithms for large-scale systems.

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Beamforming and Device Selection Design in Federated Learning With Over-the-Air Aggregation

Kalarde,

Dong,

Liang

et al. 2024

IEEE Open J. Commun. Soc.

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Federated learning (FL) with over-the-air computation can efficiently utilize the communication bandwidth but is susceptible to analog aggregation error. Excluding those devices with weak channel conditions can reduce the aggregation error, but it also limits the amount of local training data for FL, which can reduce the training convergence rate. In this work, we jointly design uplink receiver beamforming and device selection for over-the-air FL over time-varying wireless channels to maximize the training convergence rate. We reformulate this stochastic optimization problem into a mixed-integer program using an upper bound on the global training loss over communication rounds. We then propose a Greedy Spatial Device Selection (GSDS) approach, which uses a sequential procedure to select devices based on a measure capturing both the channel strength and the channel correlation to the selected devices. We show that given the selected devices, the receiver beamforming optimization problem is equivalent to downlink single-group multicast beamforming. To reduce the computational complexity, we also propose an Alternating-optimization-based Device Selection and Beamforming (ADSBF) approach, which solves the receiver beamforming and device selection subproblems alternatingly. In particular, despite the device selection being an integer problem, we are able to develop an efficient algorithm to find its optimal solution. Simulation results with real-world image classification demonstrate that our proposed methods achieve faster convergence with significantly lower computational complexity than existing alternatives. Furthermore, although ADSBF shows marginally inferior performance to GSDS, it offers the advantage of lower computational complexity when the number of devices is large.INDEX TERMS Federated learning, over-the-air aggregation, device selection, receiver beamforming.

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

Cited by 6 publications

References 26 publications

Fast Algorithm for Joint Unicast and Multicast Beamforming in Large-Scale Systems

Fast Algorithm for Joint Unicast and Multicast Beamforming in Large-Scale Systems

Ultra-Low-Complexity Algorithms with Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems

Beamforming and Device Selection Design in Federated Learning With Over-the-Air Aggregation

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