Monotonic Optimization Framework for the Two-User MISO Interference Channel

Jorswieck, Eduard A.; Larsson, Erik G.

doi:10.1109/tcomm.2010.07.090068

Cited by 79 publications

(67 citation statements)

References 27 publications

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“…In the two-user scenario, a region based on a beamforming parametrization is used in [118], which enables very efficient line-search (see also Example 3.1). If the system utility and user performance functions are concave, then a region based on interference constraints is taken in [215], which enables bounding operations based on interference-constrained beamforming (see Subsection 2.2.1).…”

Section: Remark 211 (Other Bounding Procedures)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%

See 1 more Smart Citation

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

Jorswieck

2012

FNT in Communications and Information Theory

147

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Section: Remark 211 (Other Bounding Procedures)mentioning

confidence: 99%

Section: Remark 212 (Variations)mentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

Jorswieck

2012

FNT in Communications and Information Theory

147

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“…Though in principle (17) can be solved in a centralized fashion using exhaustive searches [12] or potentially by the method in [19], the computational complexity would be very high.…”

Section: Distributed Pricing-based Precodingmentioning

confidence: 99%

Distributed precoding for MISO interference channels with channel mean feedback: Algorithms and analysis

Ding

Tirkkonen

Berry

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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Abstract-This work focuses on the design and analysis of distributed stochastic precoding algorithms for multiple-input single-output (MISO) interference channels, where each transmitter is provided with mean information of its intended channel and that of interfering channels. Unlike in cases where exact channel gains are known as in most existing works, here generalrank precoding is required for optimality instead of the rank-one beamforming. An efficient algorithm for the distributed implementation of the Nash equilibrium precoding is first proposed. A sufficient condition for this algorithm to converge to the unique equilibrium is derived for the two-user case based on stochastic ordering, and is valid for a wide range of system parameters. To improve the sum-rate performance under medium to strong interference, a pricing-based algorithm is also provided and its convergence analyzed. The two algorithms are compared in terms of sum-rate and system overhead.

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“…Recently, other authors have proposed spaces based on beamforming parametrizations [3], power allocation [4], user rates [5], and received interference [6]. The global optimization was either based on the polyblock approximation algorithm [3], [5] or the branch-and-bound algorithm [4], [6].…”

Section: Introductionmentioning

confidence: 99%

Computational framework for optimal robust beamforming in coordinated multicell systems

Björnson

Bengtsson

Zheng

et al. 2011

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

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Abstract-Coordinated beamforming can significantly improve the performance of cellular systems through joint interference management. Unfortunately, such beamforming optimization problems are typically NP-hard in multicell scenarios, making heuristic beamforming the only feasible choice in practice. This paper proposes a new branch-reduce-and-bound algorithm that solves such optimization problems globally, with a complexity suitable for benchmarking and analysis. Compared to prior work, the framework handles robustness to uncertain intercell interference and numerical analysis shows higher efficiency. I. INTRODUCTIONMost resource allocation problems in multiantenna multicell systems are non-convex and NP-hard [1], meaning that the optimal solution cannot be obtained in polynomial time. Thus, only heuristic suboptimal beamforming strategies can be applied in practice when trying to optimize, for example, the sum performance, proportional fairness, or harmonic mean. Still, it is very important to compute the optimal solution to use it as a benchmark and to study its properties.The key to solve non-convex resource allocation problems systematically is to find a suitable parameter space that represents all feasible and some infeasible strategies, and then apply some global optimization technique to iteratively reduce this space with guaranteed convergence to a global optimum In addition, all of these papers assume perfect channel state information (CSI) between all transmitters and all users in the system, which is almost impossible to achieve in practice.Herein, we propose a framework for computing optimal coordinated beamforming with robustness to CSI uncertainty. We use a new branch-reduce-and-bound (BRB) algorithm and operate in the space of the robust performance region. At each iteration, we solve a quasi-convex resource allocation problem that we call robust fairness-profile optimization (RFO). We show by simulations that the BRB algorithm is more efficient than the polyblock approximation algorithm.

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Monotonic Optimization Framework for the Two-User MISO Interference Channel

Cited by 79 publications

References 27 publications

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Distributed precoding for MISO interference channels with channel mean feedback: Algorithms and analysis

Computational framework for optimal robust beamforming in coordinated multicell systems

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