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90
Interference alignment with asymmetric complex signaling  settling the HostMadsenNosratinia conjecture
 IEEE TRANSACTION ON INFORMATION THEORY
, 2009
"... It has been conjectured by HøstMadsen and Nosratinia that complex Gaussian interference channels with constant channel coefficients have only one degreeoffreedom regardless of the number of users. While several examples are known of constant channels that achieve more than 1 degree of freedom, th ..."
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Cited by 65 (17 self)
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It has been conjectured by HøstMadsen and Nosratinia that complex Gaussian interference channels with constant channel coefficients have only one degreeoffreedom regardless of the number of users. While several examples are known of constant channels that achieve more than 1 degree of freedom, these special cases only span a subset of measure zero. In other words, for almost all channel coefficient values, it is not known if more than 1 degreeoffreedom is achievable. In this paper, we settle the HøstMadsenNosratinia conjecture in the negative. We show that at least 1.2 degreesoffreedom are achievable for all values of complex channel coefficients except for a subset of measure zero. For the class of linear beamforming and interference alignment schemes considered in this paper, it is also shown that 1.2 is the maximum number of degrees of freedom achievable on the complex Gaussian 3 user interference channel with constant channel coefficients, for almost all values of channel coefficients. To establish the achievability of 1.2 degrees of freedom we introduce the novel idea of asymmetric complex signaling i.e., the inputs are chosen to be complex but not circularly symmetric. It is shown that unlike Gaussian pointtopoint, multipleaccess and broadcast channels where circularly
Cooperative multicell precoding: Rate region characterization and distributed strategies with instantaneous and statistical CSI
 IEEE Trans. Signal Process
"... IEEE does not in any way imply IEEE endorsement of any of the Royal Institute of Technology (KTH)’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collect ..."
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Cited by 43 (1 self)
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IEEE does not in any way imply IEEE endorsement of any of the Royal Institute of Technology (KTH)’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to
Degrees of Freedom of the K User M × N MIMO Interference Channel
, 809
"... We provide innerbound and outerbound for the total number of degrees of freedom of the K user multiple input multiple output (MIMO) Gaussian interference channel with M antennas at each transmitter and N antennas at each receiver if the channel coefficients are timevarying and drawn from a continuo ..."
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Cited by 32 (4 self)
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We provide innerbound and outerbound for the total number of degrees of freedom of the K user multiple input multiple output (MIMO) Gaussian interference channel with M antennas at each transmitter and N antennas at each receiver if the channel coefficients are timevarying and drawn from a continuous distribution. The bounds are tight when the ratio max(M,N) min(M,N) = R is equal to an integer. For this case, we show that the total number of degrees of freedom is equal to min(M, N)K if K ≤ R and min(M, N) R R+1K if K> R. Achievability is based on interference alignment. We also provide examples where using interference alignment combined with zero forcing can achieve more degrees of freedom than merely zero forcing for some MIMO interference channels with constant channel coefficients. 2 I.
MultiUser MISO interference channels with SingleUser detection: Optimality of beamforming and the achievable rate region”, Submitted to
 IEEE Trans. Inform. Th
, 2009
"... For a multiuser interference channel with multiantenna transmitters and singleantenna receivers, by restricting each transmitter to Gaussian input and each receiver to a singleuser detector, computing the largest achievable rate region amounts to solving a family of nonconvex optimization probl ..."
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Cited by 29 (1 self)
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For a multiuser interference channel with multiantenna transmitters and singleantenna receivers, by restricting each transmitter to Gaussian input and each receiver to a singleuser detector, computing the largest achievable rate region amounts to solving a family of nonconvex optimization problems. Recognizing the intrinsic connection between the signal power at the intended receiver and the interference power at the unintended receiver, the original family of nonconvex optimization problems is converted into a new family of convex optimization problems. It is shown that, for such interference channels with each receiver implementing singleuser detection, transmitter beamforming can achieve all boundary points of the achievable rate region. Index terms — Gaussian interference channel, achievable rate region, beamforming I.
Sum Capacity of MIMO Interference Channels in the Low Interference Regime
, 909
"... Using Gaussian inputs and treating interference as noise at the receivers has recently been shown to be sum capacity achieving for the twouser singleinput singleoutput (SISO) Gaussian interference channel in a low interference regime, where the interference levels are below certain thresholds. In ..."
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Cited by 29 (2 self)
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Using Gaussian inputs and treating interference as noise at the receivers has recently been shown to be sum capacity achieving for the twouser singleinput singleoutput (SISO) Gaussian interference channel in a low interference regime, where the interference levels are below certain thresholds. In this paper, such a low interference regime is characterized for multipleinput multipleoutput (MIMO) Gaussian interference channels. Conditions are provided on the direct and cross channel gain matrices under which using Gaussian inputs and treating interference as noise at the receivers is sum capacity achieving. For the special cases of the symmetric multipleinput singleoutput (MISO) and singleinput multipleoutput (SIMO) Gaussian interference channels, more explicit expressions for the low interference regime are derived. In particular, the threshold on the interference levels that characterize low interference regime is related to the input SNR and the angle between the direct and cross channel gain vectors. It is shown that the low interference regime can be quite significant for MIMO interference channels, with the low interference threshold being at least as large as the sine of the angle between the direct and cross channel gain vectors for the MISO and SIMO cases. I.
Robust monotonic optimization framework for multicell MISO systems
 IEEE Trans. Signal Process
, 2012
"... Abstract—The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NPhard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constra ..."
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Cited by 21 (3 self)
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Abstract—The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NPhard, 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 branchreduceandbound (BRB) algorithm handles general multicell downlink systems with singleantenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robustness to channel uncertainty. A robust fairnessprofile optimization (RFO) problem is solved at each iteration, which is a quasiconvex problem and a novel generalization of maxmin 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 zeroforcing solution to the general problem. Index Terms—Branchreduceandbound, dynamic cooperation clusters, fairnessprofile, Network MIMO, optimal resource allocation, performance region, worstcase robustness.
Balancing egoism and altruism on interference channel: The MIMO case
 in 2010 IEEE International Conference on Communications (ICC
, 2010
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Distributed Resource Allocation Schemes: Pricing Algorithms for Power Control and Beamformer Design in Interference Networks
"... Achieving high spectral efficiencies in wireless networks requires the ability to mitigate and manage the associated interference. This becomes especially important in networks where many transmitters and receivers are randomly placed, so that in the absence of coordination a particular receiver is ..."
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Cited by 15 (0 self)
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Achieving high spectral efficiencies in wireless networks requires the ability to mitigate and manage the associated interference. This becomes especially important in networks where many transmitters and receivers are randomly placed, so that in the absence of coordination a particular receiver is likely to encounter significant interference from a neighboring transmitter. A challenge is then to provide a means
Achieving global optimality for weighted sumrate maximization in the Kuser Gaussian interference channel with multiple antennas
 IEEE Trans. Wireless Commun
, 2012
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Optimal distributed beamforming for MISO interference channels
 IEEE Trans. Signal Process
, 2011
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