Robust Transmit Beamforming for Multigroup Multicasting

Chen, Zhenyuan; Zhang, Wenyi; Wei, Guo

doi:10.1109/vtcfall.2012.6398962

Cited by 9 publications

(28 citation statements)

References 15 publications

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“…However, by applying the S-lemma [19], the error vector in (22) can be eliminated. This procedure is analytically described in [27]. Thus, can be converted to a SDP and solved efficiently using the methods described in Section III.…”

Section: E Robust Design Under Pacsmentioning

confidence: 99%

Weighted Fair Multicast Multigroup Beamforming Under Per-antenna Power Constraints

Christopoulos

Chatzinotas

Ottersten

2014

IEEE Trans. Signal Process.

172

187

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A multiantenna transmitter that conveys independent sets of common data to distinct groups of users is considered. This model is known as physical layer multicasting to multiple cochannel groups. In this context, the practical constraint of a maximum permitted power level radiated by each antenna is addressed. The per-antenna power constrained system is optimized in a maximum fairness sense with respect to predetermined quality of service weights. In other words, the worst scaled user is boosted by maximizing its weighted signal-to-interference plus noise ratio. A detailed solution to tackle the weighted max-min fair multigroup multicast problem under per-antenna power constraints is therefore derived. The implications of the novel constraints are investigated via prominent applications and paradigms. What is more, robust per-antenna constrained multigroup multicast beamforming solutions are proposed. Finally, an extensive performance evaluation quantifies the gains of the proposed algorithm over existing solutions and exhibits its accuracy over per-antenna power constrained systems.Index Terms-Gaussian randomization, per-antenna power constraints, physical multigroup multicasting, semidefinite relaxation, weighted max-min fair optimization.

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Section: E Robust Design Under Pacsmentioning

confidence: 99%

Weighted Fair Multicast Multigroup Beamforming Under Per-antenna Power Constraints

Christopoulos

Chatzinotas

Ottersten

2014

IEEE Trans. Signal Process.

172

187

View full text Add to dashboard Cite

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“…We choose ξ 1 = 10 −3 , ξ 2 = 10 −3 , C ig = 1/µ 2 I M and σ 2 ig = 1, i ∈ N g , g ∈ G, γ = M Watt in the SP constraints, γ = 1 Watt in the PAP constraints. We consider three baseline schemes, i.e., the non-robust MM-based method (which treats the estimated channel as the true channel when designing the beamformer) by extending the method in [4] and the SDRbased method in [5] in the case of SP or in [6] in the case of PAP with the number of Gaussian randomizations N rand being 20 and 100, respectively. It is difficult to obtain the worst-case minimum user rate for the non-robust MM-based method which requires solving a non-convex problem with infinitely many constraints e ig ∈ S ig , i ∈ N g , g ∈ G. Thus for fair comparison, we generate 1000 error realizations for each channel realization and choose the minimum user rate over these error realizations as the worst-case user rate for all schemes, as in [6].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Denote e (e ig ) i∈Ng ,g∈G . Here we consider the widely used elliptic bounded error model, i.e., e H ig C ig e ig ≤ 1, where C ig ∈ S M is a Hermitian positive definite matrix which specifies the size and shape of the ellipsoid bound [5], [6]. Denote S ig {e ig | e H ig C ig e ig ≤ 1}.…”

Section: System Modelmentioning

confidence: 99%

“…Lemma 1: 1 where µ is a positive constant and I M denotes the M × M identity matrix, if w satisfies (3), then it satisfies (5). Proof: Using triangle inequality, we have:…”

Section: Power Minimization With Sinr Constraintsmentioning

confidence: 99%

“…Problem P App-PM is non-convex due to the non-convexity of (5). We develop an algorithm (Algorithm 1) to obtain a stationary point of Problem P App-PM using the MM approach in [7].…”

Section: Power Minimization With Sinr Constraintsmentioning

confidence: 99%

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Low Complexity Algorithms for Robust Multigroup Multicast Beamforming

et al. 2019

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Existing methods for robust multigroup multicast beamforming obtain feasible points using semidefinite relaxation (SDR) and Gaussian randomization, and have high computational complexity. In this letter, we consider the robust multigroup multicast beamforming design to minimize the sum power (SP) or per-antenna power (PAP) under the signal-to-interference-plusnoise ratio (SINR) constraints and to maximize the worst-case SINR under the SP constraint or PAP constraints, respectively. The resulting optimization problems are challenging non-convex problems with infinitely many constraints. For each problem, using the majorization-minimization (MM) approach, we propose an iterative algorithm to obtain a feasible solution which is shown to be a stationary point under certain conditions. We also show that the proposed algorithms have much lower computational complexity than existing SDR-based algorithms.

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