Parameterization of the MISO IFC rate region: the case of partial channel state information

Lindblom, Johannes; Larsson, Erik G.; Jorswieck, Eduard A.

doi:10.1109/twc.2010.02.081371

Cited by 28 publications

(29 citation statements)

References 14 publications

(20 reference statements)

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“…Moreover, the parameterization for the Paretooptimal beam vector is compressed from K(K − 1) complex numbers [4] to K(K − 1) real numbers. In addition to these results, other interesting works for MISO ICs include the consideration of imperfect CSI [6], shared data [7], second-order cone programming [8], etc. Although these works provide significant theoretical insights into the optimal beam structure and parameterization of Pareto-optimal beam vectors, it is not easy to use these results to design an optimal beam vector in the real-world systems, and the beam design problem in the general case still remains as a non-trivial problem practically.…”

Section: Introductionmentioning

confidence: 92%

Coordinated Beamforming With Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control

Park

Lee

Sung

et al. 2013

IEEE Trans. Signal Process.

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In this paper, coordinated beamforming based on relaxed zero forcing (RZF) for K transmitterreceiver pair multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) interference channels is considered. In the RZF coordinated beamforming, conventional zero-forcing interference leakage constraints are relaxed so that some predetermined interference leakage to undesired receivers is allowed in order to increase the beam design space for larger rates than those of the zero-forcing (ZF) scheme or to make beam design feasible when ZF is impossible. In the MISO case, it is shown that the rate-maximizing beam vector under the RZF framework for a given set of interference leakage levels can be obtained by sequential orthogonal projection combining (SOPC). Based on this, exact and approximate closed-form solutions are provided in two-user and three-user cases, respectively, and an efficient beam design algorithm for RZF coordinated beamforming is provided in general cases.Furthermore, the rate control problem under the RZF framework is considered. A centralized approach and a distributed heuristic approach are proposed to control the position of the designed rate-tuple in the achievable rate region. Finally, the RZF framework is extended to MIMO interference channels by deriving a new lower bound on the rate of each user.

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Section: Introductionmentioning

confidence: 92%

Coordinated Beamforming With Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control

Park

Lee

Sung

et al. 2013

IEEE Trans. Signal Process.

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“…Receivers here perform single-user decoding and treat interference as noise. With statistical feedback, the performance measure of interest is the ergodic rate in the i-th link [12]:…”

Section: System Model and Problem Statementmentioning

confidence: 99%

“…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%

“…The CCI and CMI at the transmitters are synonymous to channel covariance feedback and channel mean feedback, respectively [10] [11]. Regarding the studies on MISO interference channels with CCI, to date, only a necessary condition has been derived in [12] for precoding to achieve rates on the Pareto boundary; specific precoding strategies have not been proposed. Assuming channel mean feedback, stochastic zero forcing and Nash equilibrium precoding strategies have been proposed in [13]; however, the uniqueness of the equilibrium and its efficient implementation have not been addressed.…”

Section: Introductionmentioning

confidence: 99%

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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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“…We assume that transmission consists of scalar coding followed by beamforming 1 and that all propagation channels are 1 This is optimal in the case of instantaneous CSI, but not necessarily for statistical CSI, see [5].…”

Section: Preliminaries a System Modelmentioning

confidence: 99%

Cooperative beamforming for the MISO interference channel

Lindblom

Karipidis

2010

2010 European Wireless Conference (EW)

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Abstract-A distributed beamforming algorithm is proposed for the two-user multiple-input single-output (MISO) interference channel (IFC). The algorithm is iterative and uses as bargaining value the interference that each transmitter generates towards the receiver of the other user. It enables cooperation among the transmitters in order to increase both users' rates by lowering the overall interference. In every iteration, as long as both rates keep on increasing, the transmitters mutually decrease the generated interference. They choose their beamforming vectors distributively, solving the constrained optimization problem of maximizing the useful signal power for a given level of generated interference. The algorithm is equally applicable when the transmitters have either instantaneous or statistical channel state information (CSI). The difference is that the core optimization problem is solved in closed-form for instantaneous CSI, whereas for statistical CSI an efficient solution is found numerically via semidefinite programming. The outcome of the proposed algorithm is approximately Pareto-optimal. Extensive numerical illustrations are provided, comparing the proposed solution to the Nash equilibrium, zero-forcing, Nash bargaining, and maximum sum-rate operating points.

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Parameterization of the MISO IFC rate region: the case of partial channel state information

Cited by 28 publications

References 14 publications

Coordinated Beamforming With Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control

Coordinated Beamforming With Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control

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

Cooperative beamforming for the MISO interference channel

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