Fast Approximation Algorithms for a Class of Non-convex QCQP Problems Using First-Order Methods

Konar, Aritra; Sidiropoulos, Nicholas D.

doi:10.1109/tsp.2017.2690386

Cited by 38 publications

(29 citation statements)

References 34 publications

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“…On the other hand, [20] has studied the max-min fair multicast beamforming problem, when N > M . This case is applicable in massive MIMO scenarios, [38], [39], where the base station is equipped with a large number of antennas.…”

Section: A Basic Modelmentioning

confidence: 99%

“…which is still non-convex. In order to approximate (4), the authors of [20] proposed a SCA-approach using specialized FOMs to solve each subproblem. For large N , this approach in [20] demonstrated considerable computational savings compared to SDR and SCA using generic convex programming solvers for solving each subproblem.…”

Section: A Basic Modelmentioning

confidence: 99%

“…The cardinality constraint (5c) is non-convex, and nondifferentiable, which renders the problem more computationally challenging. One possible approach is to apply exhaustive search over the N K possible antenna subsets, followed by solving a reduced problem of the form (4), using the algorithm in [20], for each subset. However, the computational complexity of exhaustive search becomes prohibitive even for modest values of N , which renders this approach impractical.…”

Section: B Antenna Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Ibrahim

Konar

Sidiropoulos

2020

IEEE Trans. Signal Process.

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Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are switched to an appropriate subset of antennas. This paper considers the joint problem of multicast beamforming and antenna selection for a single multicast group in massive MIMO systems. The prior state-of-art for this problem relies on semi-definite relaxation (SDR), which cannot scale up to the massive MIMO regime. A successive convex approximation (SCA) based approach is proposed to tackle max-min fair joint multicast beamforming and antenna selection. The key idea of SCA is to successively approximate the non-convex problem by a class of non-smooth, convex optimization problems. Two fast and memory efficient first-order methods are proposed to solve each SCA subproblem. Simulations demonstrate that the proposed algorithms outperform the existing state-of-art approach in terms of solution quality and run time, in both traditional and especially in massive MIMO settings.

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Section: A Basic Modelmentioning

confidence: 99%

Section: A Basic Modelmentioning

confidence: 99%

Section: B Antenna Selectionmentioning

confidence: 99%

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Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Ibrahim

Konar

Sidiropoulos

2020

IEEE Trans. Signal Process.

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“…The problem was shown to be NP-Hard in [1]; however, a high quality approximate solution was developed based on the Semi-Definite Relaxation (SDR) technique [3]. On the other hand, the problem of max-min "fair" single group multicast beamforming subject to per antenna power constraints was studied in [2]. The authors considered a different approximation approach namely Successive Convex Approximation (SCA) [4].…”

mentioning

confidence: 99%

“…Then each SCA subproblem is solved using a modified version of the Alternating Direction Method of Multipliers algorithm (ADMM). In addition to ADMM, [2] also considered the SP-MP method for max-min fair multicasting.…”

mentioning

confidence: 99%

Mirror-Prox SCA Algorithm for Multicast Beamforming and Antenna Selection

Ibrahim

Konar

Mei

et al. 2018

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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This paper considers the (NP-)hard problem of joint multicast beamforming and antenna selection. Prior work has focused on using Semi-Definite relaxation (SDR) techniques in an attempt to obtain a high quality sub-optimal solution. However, SDR suffers from the drawback of having high computational complexity, as SDR lifts the problem to higher dimensional space, effectively squaring the number of variables. This paper proposes a high performance, low complexity Successive Convex Approximation (SCA) algorithm for max-min SNR "fair" joint multicast beamforming and antenna selection under a sum power constraint. The proposed approach relies on iteratively approximating the non-convex objective with a series of non-smooth convex subproblems, and then, a first order-based method called Saddle Point Mirror-Prox (SP-MP) is used to compute optimal solutions for each SCA subproblem. Simulations reveal that the SP-MP SCA algorithm provides a higher quality and lower complexity solution compared to the one obtained using SDR.

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System error estimation for sensor network with integrated sensing and communication application

et al. 2023

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Fast Approximation Algorithms for a Class of Non-convex QCQP Problems Using First-Order Methods

Cited by 38 publications

References 34 publications

Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Mirror-Prox SCA Algorithm for Multicast Beamforming and Antenna Selection

System error estimation for sensor network with integrated sensing and communication application

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