Closed-form parameterization of the Pareto boundary for the two-user MISO interference channel

Lindblom, Johannes; Karipidis, Eleftherios; Larsson, Erik G.

doi:10.1109/icassp.2011.5947108

Cited by 29 publications

(38 citation statements)

References 11 publications

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“…To complete the picture, note that there are special cases when the Pareto boundary can be characterized in a necessary and sufficient manner without the need for numerically solving an optimization problem for each point. Example 3.1 showed that this is possible for the two-user interference channel with per-transmitter power constraints [149,181] and Corollary 3.7 showed it for L =1.…”

Section: Necessary and Sufficient Pareto Boundary Parametrizationmentioning

confidence: 99%

“…Example 2.6 (ǫ-Constraint Optimization). The ǫ-constraint optimization represents maximizing the performance of MS k , while guaranteeing that g i ≥ ǫ i for all i [38,98,123,149,278,292,293]. This problem is solved by Theorem 2.10 using r k (τ )=τ + ǫ k and r i (τ )=ǫ i .…”

Section: Quasi-fixed Quality-of-service Requirementsmentioning

confidence: 99%

“…Beamforming is thus a balance between selfish MRT and altruistic ZFBF. Based on the parametrization in (3.3), a closed-form expression for the performance region R was derived independently in [149] and [181,Theorem 2]. The achievable SINR values for both users can be written as a function of the parameters λ 1 and λ 2 : SINR 1 (λ 1 ,λ 2 ), SINR 2 (λ 1 ,λ 2 ).…”

Section: Definition 31 (Orthogonal Projection)mentioning

confidence: 99%

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Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

298

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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Section: Necessary and Sufficient Pareto Boundary Parametrizationmentioning

confidence: 99%

Section: Quasi-fixed Quality-of-service Requirementsmentioning

confidence: 99%

Section: Definition 31 (Orthogonal Projection)mentioning

confidence: 99%

See 1 more Smart Citation

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

298

317

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“…The -constraint optimization represents maximizing the performance of MS k , while guaranteeing that g i ≥ i for all i [38,98,123,149,278,292,293]. This problem is solved by Theorem 2.10 using r k (τ ) = τ + k and r i (τ ) = i .…”

Section: Example 26 ( -Constraint Optimization)mentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

Jorswieck

2012

FNT in Communications and Information Theory

144

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“…In [3] we computed an arbitrary point on the boundary via a sequence of second-order cone (SOC) programs. In [4], we gave a closed-form parameterization of the beamforming vectors that yield PO rate points. The methods for finding R dn , R nd , and R dd devised in the sequel are novel.…”

Section: Efficient Computation Of the Pareto Boundarymentioning

confidence: 99%

Efficient computation of the pareto boundary for the two-user MISO interference channel with multi-user decoding capable receivers

Lindblom

Karipidis

Larsson

2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-We study the two-user multiple-input single-output (MISO) interference channel for the scenario where the transmitters have perfect channel state information and employ singlestream beamforming. We assume that the receivers are able of decoding the data from both transmitters. Hence, the signal from the interfering transmitter might be decoded, treating the desired signal as noise, and subtracted from the received signal. We propose an efficient method for finding the Pareto boundary of the corresponding achievable rate region. This method has a complexity which is constant in the number of transmit antennas.

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Closed-form parameterization of the Pareto boundary for the two-user MISO interference channel

Cited by 29 publications

References 11 publications

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Efficient computation of the pareto boundary for the two-user MISO interference channel with multi-user decoding capable receivers

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