Utility Maximization in the Multi-User MISO Downlink with Linear Precoding

Brehmer, Johannes; Utschick, Wolfgang

doi:10.1109/icc.2009.5199561

Cited by 12 publications

(11 citation statements)

References 8 publications

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“…The dimension of the feasibility problem can be reduced under perfect CSI and other special conditions (see Appendix A and Section III), but as the BRB algorithm solves a long sequence of convex subproblems the total complexity makes it unsuitable for real-time applications. However, we show numerically in Section VI that the proposed algorithm has far better convergence behavior than the outer polyblock approximation in [24]- [27].…”

Section: Final Algorithmmentioning

confidence: 96%

“…However, closed form expressions can be attained in many cases. For example, weighted sum performance with (with weights ) gives (24) where the min-operator makes sure that .…”

Section: B Reductionmentioning

confidence: 99%

“…The framework can be applied for any system performance measure that increases monotonically in the performance of each user, which of course is satisfied by all reasonable measures. Convergence to the global optimum is achieved through a branch-reduce-and-bound (BRB) algorithm that builds upon previous work in [22], and we show far better convergence than the polyblock algorithm in [24]- [27]. Each iteration of the BRB algorithm solves a quasiconvex subproblem.…”

Section: Introductionmentioning

confidence: 95%

“…However, such problems can still be solved with global convergence and optimality using the framework of monotonic optimization, developed in [21] and [22]. The outer polyblock approximation is an algorithm in this framework [21], and applications to single-cell [23], [24] and multicell transmission [25]- [27] with perfect CSI have appeared in literature. Unfortunately, the polyblock algorithm requires a very large number of iterations to achieve accurate results, thus limiting usage to systems with no more than a handful of users [22].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Monotonic Optimization Framework for Multicell MISO Systems

Björnson

Zheng

Bengtsson

et al. 2012

IEEE Trans. Signal Process.

130

230

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The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robustness to channel uncertainty. A robust fairness-profile optimization (RFO) problem is solved at each iteration, which is a quasiconvex problem and a novel generalization of max-min fairness. The BRB algorithm is computationally costly, but it shows better convergence than the previously proposed outer polyblock approximation algorithm. Our framework is suitable for computing benchmarks in general multicell systems with or without channel uncertainty. We illustrate this by deriving and evaluating a zero-forcing solution to the general problem.

QC 20120525

show abstract

Section: Final Algorithmmentioning

confidence: 96%

“…However, closed form expressions can be attained in many cases. For example, weighted sum performance with (with weights ) gives (24) where the min-operator makes sure that .…”

Section: B Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Monotonic Optimization Framework for Multicell MISO Systems

Björnson

Zheng

Bengtsson

et al. 2012

IEEE Trans. Signal Process.

130

230

View full text Add to dashboard Cite

QC 20120525

show abstract

“…However, results in [41] are limited to the two-user case. The application of the framework to the MISO broadcast channel was first published in [42], and is discussed in detail in Sect. The application of the framework to the MISO broadcast channel was first published in [42], and is discussed in detail in Sect.…”

Section: Related Workmentioning

confidence: 99%

Utility Maximization in Nonconvex Wireless Systems

Brehmer¹

2012

Foundations in Signal Processing, Communications and Networking

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Energy-efficient rate balancing in vector broadcast channels with linear transceivers

Utschick

2012

2012 International Symposium on Wireless Communication Systems (ISWCS)

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We consider the minimization of the energy per bit (or, equivalently, the maximization of the energy efficiency) in a multiple-input single-output (MISO) broadcast channel with rate balancing constraints, i.e., in a multiuser system where certain fixed ratios between the rates of the various users have to be kept. This paper deals with the practically relevant case where the transmit strategy is restricted to simple linear filtering without time-sharing, i.e., sophisticated coding schemes such as dirty paper coding (DPC) are not allowed. Even though the arising optimization problem is non-convex, it can be solved with reasonable complexity in a globally optimal manner using the algorithm proposed in this paper. This globally optimal solution enables us to study the impact of rate balancing constraints on the energy efficiency of a vector broadcast channel in numerical simulations.

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Utility Maximization in the Multi-User MISO Downlink with Linear Precoding

Cited by 12 publications

References 8 publications

Robust Monotonic Optimization Framework for Multicell MISO Systems

Robust Monotonic Optimization Framework for Multicell MISO Systems

Utility Maximization in Nonconvex Wireless Systems

Energy-efficient rate balancing in vector broadcast channels with linear transceivers

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