Utility Maximization in Nonconvex Wireless Systems

Brehmer, Johannes

doi:10.1007/978-3-642-17438-4

Cited by 28 publications

(47 citation statements)

References 59 publications

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“…An example is single-cell transmission with a total power constraint [39], or any multi-cell scenario with only one power constraint.…”

Section: Proof the Normalized Directionvmentioning

confidence: 99%

“…In practice, it might be necessary to stop the algorithm before it converges, but fortunately f min is usually closer to the true optimal value than f max (as noted earlier). [118,276]; (c) the original problem can be perturbed in an adaptive manner to further avoid shallow cuts and jamming [39,275]; (d) scheduling can be included in the algorithm [39,276]; and (e) the algorithm can be restarted from the current best solution if the number of vertices grows too large [274].…”

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confidence: 99%

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Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

302

317

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The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gain of employing multiple antennas is well-recognized: the data throughput increases linearly with the number of transmit antennas if the spatial dimension is utilized to serve many users in parallel. The practical performance of multi-cell systems is, however, limited by a variety of nonidealities, such as insufficient channel knowledge, high computational complexity, heterogeneous user conditions, limited backhaul capacity, transceiver impairments, and the constrained level of coordination between base stations. This tutorial presents a general framework for modeling different multi-cell scenarios, including clustered joint transmission, coordinated beamforming, interference channels, cognitive radio, and spectrum sharing between operators. The framework enables joint analysis and insights that are both scenario independent and dependent.The performance of multi-cell systems depends on the resource allocation; that is, how the time, power, frequency, and spatial resources are divided among users. A comprehensive characterization of resource allocation problem categories is provided, along with the signal processing algorithms that solve them. The inherent difficulties are revealed: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between maximizing aggregate system throughput and maintaining user fairness. The tutorial provides a pragmatic foundation for resource allocation where the system utility metric can be selected to achieve practical feasibility. The structure of optimal resource allocation is also derived, in terms of beamforming parameterizations and optimal operating points.This tutorial provides a solid ground and understanding for optimization of practical multi-cell systems, including the impact of the nonidealities mentioned above. The Matlab code is available online for some of the examples and algorithms in this tutorial. IntroductionThis section describes a general framework for modeling different types of multi-cell systems and measuring their performance -both in terms of system utility and individual user performance. The framework is based on the concept of dynamic cooperation clusters, which enables unified analysis of everything from interference channels and cognitive radio to cellular networks with global joint transmission. The concept of resource allocation is defined as allocating transmit power among users and spatial directions, while satisfying a set of power constraints that have physical, regulatory, and economic implications. A major complication in resource allocation is the inter-user interference that arises and limits the performance when multiple users are served in parallel. Resource allocation is particularly complex when multiple antennas are employed at each base station. However, the throu...

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“…An example is single-cell transmission with a total power constraint [39], or any multi-cell scenario with only one power constraint.…”

Section: Proof the Normalized Directionvmentioning

confidence: 99%

mentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

302

317

View full text Add to dashboard Cite

show abstract

“…However, if we do not impose any restrictions on f (·) then we generally cannot even find an ε-optimal solution in finite time [39].…”

Section: Remark 210 (Importance Of Lipschitz Continuity)mentioning

confidence: 99%

“…There are many variations on the PA algorithm that might improve the convergence speed: (a) An improved vertex update rule is suggested in [275, Proposition 4.2] to remove more in each iteration; (b) the line-search accuracy δ can be a function of the number of iterations and the vertex g (n) [118,276]; (c) the original problem can be perturbed in an adaptive manner to further avoid shallow cuts and jamming [39,275]; (d) scheduling can be included in the algorithm [39,276]; and (e) the algorithm can be restarted from the current best solution if the number of vertices grows too large [274].…”

Section: Remark 212 (Variations)mentioning

confidence: 99%