Multicell Multicast Beamforming with Delayed SNR Feedback

Jordan, Markus; Gong, Xitao; Ascheid, Gerd

doi:10.1109/glocom.2009.5425297

Cited by 10 publications

(11 citation statements)

References 9 publications

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“…This problem later has been extended to multi-group multicast [7][8][9] , and multi-cell networks [10,11]. It has also been considered in different networks such as relay networks [12][13][14][15], cognitive radio networks [16][17][18][19] and cloud-radio networks [20].…”

Section: Author's Declarationmentioning

confidence: 99%

“…Beamforming improves the spectrum efficiency and data rate in massive MIMO systems. In summary, beamforming can bring better perfomance and reduced interference to the system and has been studied widely in several different communication networks [5][6][7][8][9][10][11]21,[31][32][33][34][35][36][37]. The focus of this thesis is on joint unicast and multicast beamforming.…”

Section: Beamformingmentioning

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: Author's Declarationmentioning

confidence: 99%

Section: Beamformingmentioning

confidence: 99%

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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“…The design of multicast beamforming was initially considered for a single user group [3,4]. It was later extended to multiple user groups [5][6][7], relay networks [8], and multi-cell networks [9,10], with the additional challenge on inter-group or inter-cell interference mitigation. The family of multicast beamforming problems is NPhard [3].…”

Section: Introductionmentioning

confidence: 99%

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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“…By linking the MMS problem with a weighted peak power minimization (WPPM) problem, we propose an efficient algorithm to find the near-optimal solution. Previously, the authors in [11] [12] also studied the multicell multicast beamforming problem with the objective to maximize the minimum SINR but subject to a total sum power constraint across all base stations. Hence, the problem considered in [11] [12] is very similar to the multicast beamforming problem in single-cell multi-group systems as in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Previously, the authors in [11] [12] also studied the multicell multicast beamforming problem with the objective to maximize the minimum SINR but subject to a total sum power constraint across all base stations. Hence, the problem considered in [11] [12] is very similar to the multicast beamforming problem in single-cell multi-group systems as in [2]. Authors in [13] also considered the similar problem under per-base-station power constraint but allowed data sharing among base stations, which is similar to the "network MIMO" and requires a large amount of information exchange overhead.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Multicast Beamforming in Multicell Networks

Xiang

Tao

Wang

2013

IEEE Trans. Wireless Commun.

155

169

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We study physical layer multicasting in multicell networks where each base station, equipped with multiple antennas, transmits a common message using a single beamformer to multiple users in the same cell. We investigate two coordinated beamforming designs: the quality-of-service (QoS) beamforming and the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The goal of the QoS beamforming is to minimize the total power consumption while guaranteeing that received SINR at each user is above a predetermined threshold. We present a necessary condition for the optimization problem to be feasible. Then, based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. The max-min SINR (MMS) beamforming is to maximize the minimum received SINR among all users under per-base station power constraints. We show that the MMS problem and a weighted peak-power minimization (WPPM) problem are inverse problems. Based on this inversion relationship, we then propose an efficient algorithm to solve the MMS problem in an approximate manner. Simulation results demonstrate significant advantages of the proposed multicast beamforming algorithms over conventional multicasting schemes.Index Terms-Physical layer multicasting, coordinated beamforming, quality of service (QoS), max-min SINR (MMS), semidefinite programming (SDP).

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Multicell Multicast Beamforming with Delayed SNR Feedback

Cited by 10 publications

References 9 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

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

Coordinated Multicast Beamforming in Multicell Networks

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