Multicell Coordinated Beamforming with Rate Outage Constraint--Part II: Efficient Approximation Algorithms

This paper studies the coordinated beamforming (CoBF) design for the multiple-input single-output interference channel, provided that only channel distribution information is known to the transmitters. The problem under consideration is a probabilistically constrained optimization problem which maxi...

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Veröffentlicht in:arXiv.org
Hauptverfasser: Wei-Chiang, Li, Tsung-Hui, Chang, Chong-Yung, Chi
Format: Paper
Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 15.05.2014
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ISSN:2331-8422
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Abstract This paper studies the coordinated beamforming (CoBF) design for the multiple-input single-output interference channel, provided that only channel distribution information is known to the transmitters. The problem under consideration is a probabilistically constrained optimization problem which maximizes a predefined system utility subject to constraints on rate outage probability and power budget of each transmitter. Our recent analysis has shown that the outage-constrained CoBF problem is intricately difficult, e.g., NP-hard. Therefore, the focus of this paper is on suboptimal but computationally efficient algorithms. Specifically, by leveraging on the block successive upper bound minimization (BSUM) method in optimization, we propose a Gauss-Seidel type algorithm, called distributed BSUM algorithm, which can handle differentiable, monotone and concave system utilities. By exploiting a weighted minimum mean-square error (WMMSE) reformulation, we further propose a Jocobi-type algorithm, called distributed WMMSE algorithm, which can optimize the weighted sum rate utility in a fully parallel manner. To provide a performance benchmark, a relaxed approximation method based on polyblock outer approximation is also proposed. Simulation results show that the proposed algorithms are significantly superior to the existing successive convex approximation method in both performance and computational efficiency, and can yield promising approximation performance.
AbstractList This paper studies the coordinated beamforming (CoBF) design for the multiple-input single-output interference channel, provided that only channel distribution information is known to the transmitters. The problem under consideration is a probabilistically constrained optimization problem which maximizes a predefined system utility subject to constraints on rate outage probability and power budget of each transmitter. Our recent analysis has shown that the outage-constrained CoBF problem is intricately difficult, e.g., NP-hard. Therefore, the focus of this paper is on suboptimal but computationally efficient algorithms. Specifically, by leveraging on the block successive upper bound minimization (BSUM) method in optimization, we propose a Gauss-Seidel type algorithm, called distributed BSUM algorithm, which can handle differentiable, monotone and concave system utilities. By exploiting a weighted minimum mean-square error (WMMSE) reformulation, we further propose a Jocobi-type algorithm, called distributed WMMSE algorithm, which can optimize the weighted sum rate utility in a fully parallel manner. To provide a performance benchmark, a relaxed approximation method based on polyblock outer approximation is also proposed. Simulation results show that the proposed algorithms are significantly superior to the existing successive convex approximation method in both performance and computational efficiency, and can yield promising approximation performance.
Author Chong-Yung, Chi
Wei-Chiang, Li
Tsung-Hui, Chang
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Snippet This paper studies the coordinated beamforming (CoBF) design for the multiple-input single-output interference channel, provided that only channel distribution...
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SubjectTerms Algorithms
Approximation
Beamforming
Computational efficiency
Computer simulation
Computing time
Constraints
Mathematical analysis
Optimization
Transmitters
Upper bounds
Utilities
Title Multicell Coordinated Beamforming with Rate Outage Constraint--Part II: Efficient Approximation Algorithms
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