Results 1  10
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28
Spectrummanagement in multiuser cognitive wireless networks: Optimality and algorithms
 IEEE J. Selected Areas Commun
"... Abstract—Spectrum management is used to improve performance in multiuser communication system, e.g., cognitive radio or femtocell networks, where multiuser interference can lead to data rate degradation. We study the nonconvex NPhard problem of maximizing a weighted sum rate in a multiuser Gaussia ..."
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Cited by 27 (11 self)
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Abstract—Spectrum management is used to improve performance in multiuser communication system, e.g., cognitive radio or femtocell networks, where multiuser interference can lead to data rate degradation. We study the nonconvex NPhard problem of maximizing a weighted sum rate in a multiuser Gaussian interference channel by power control subject to affine power constraints. By exploiting the fact that this problem can be restated as an optimization problem with constraints that are spectral radii of specially crafted nonnegative matrices, we derive necessary and sufficient optimality conditions and propose a global optimization algorithm based on the outer approximation method. Central to our techniques is the use of nonnegative matrix theory, e.g., nonnegative matrix inequalities and the PerronFrobenius theorem. We also study an inner approximation method and a relaxation method that give insights to special cases. Our techniques and algorithm can be extended to a multiple carrier system model, e.g., OFDM system or receivers with interference suppression capability. Index Terms—Optimization, nonnegative matrix theory, dynamic spectrum access, power control, cognitive wireless networks. I.
Maximizing Sum Rate and Minimizing MSE on Multiuser Downlink: Optimality, Fast Algorithms and Equivalence via Maxmin SIR
"... Maximizing the minimum weighted SIR, minimizing the weighted sum MSE and maximizing the weighted sum rate in a multiuser downlink system are three important performance objectives in joint transceiver and power optimization, where all the users have a total power constraint. We show that, through co ..."
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Cited by 27 (13 self)
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Maximizing the minimum weighted SIR, minimizing the weighted sum MSE and maximizing the weighted sum rate in a multiuser downlink system are three important performance objectives in joint transceiver and power optimization, where all the users have a total power constraint. We show that, through connections with the nonlinear PerronFrobenius theory, jointly optimizing power and beamformers in the maxmin weighted SIR problem can be solved optimally in a distributed fashion. Then, connecting these three performance objectives through the arithmeticgeometric mean inequality and nonnegative matrix theory, we solve the weighted sum MSE minimization and the weighted sum rate maximization in the low to moderate interference regimes using fast algorithms. In the general case, we first establish the optimality conditions to the weighted sum MSE minimization and the weighted sum rate maximization problems and provide their further connection to the maxmin weighted SIR problem. We then propose a distributed weighted proportional SIR algorithm that leverages our fast maxmin weighted SIR algorithm to solve the two nonconvex problems, and give conditions under which global optimality is achieved. Numerical results are provided to complement the analysis.
A unified analysis of maxmin weighted SINR for MIMO downlink system
 IEEE Trans. Signal Process
, 2011
"... Abstract—This paper studies the maxmin weighted signaltointerferenceplusnoise ratio (SINR) problem in the multipleinputmultipleoutput (MIMO) downlink, where multiple users are weighted according to priority and are subject to a weightedsumpower constraint. First, we study the multiplein ..."
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Cited by 13 (4 self)
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Abstract—This paper studies the maxmin weighted signaltointerferenceplusnoise ratio (SINR) problem in the multipleinputmultipleoutput (MIMO) downlink, where multiple users are weighted according to priority and are subject to a weightedsumpower constraint. First, we study the multipleinputsingleoutput (MISO) and singleinputmultipleoutput (SIMO) problems using nonlinear Perron–Frobenius theory. As a byproduct, we solve the open problem of convergence for a previously proposed MISO algorithm by Wiesel, Eldar, and Shamai in 2006. Furthermore, we unify our analysis with respect to the previous alternate optimization algorithm proposed by Tan, Chiang, and Srikant in 2009, by showing that our MISO result can, in fact, be derived from their algorithm. Next, we combine our MISO and SIMO results into an algorithm for the MIMO problem. We show that our proposed algorithm is optimal when the channels are rankone, or when the network is operating in the low signaltonoise ratio (SNR) region. Finally, we prove the parametric continuity of the MIMO problem in the power constraint, and we use this insight to propose a heuristic initialization strategy for improving the performance of our (generally) suboptimal MIMO algorithm. The proposed initialization strategy exhibits improved performance over random initialization. Index Terms—Beamforming, multipleinput–multipleoutput (MIMO), uplink–downlink duality.
Optimal power control in Rayleighfading heterogeneous networks
 in Proc. IEEE INFOCOM
, 2011
"... Abstract—Heterogeneous wireless networks employ varying degrees of network coverage using power control in a multitier configuration, where lowpower femtocells are used to enhance performance, e.g., optimize outage probability. We study the worst outage probability problem under Rayleigh fading. A ..."
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Cited by 13 (5 self)
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Abstract—Heterogeneous wireless networks employ varying degrees of network coverage using power control in a multitier configuration, where lowpower femtocells are used to enhance performance, e.g., optimize outage probability. We study the worst outage probability problem under Rayleigh fading. As a byproduct, we solve an open problem of convergence for a previously proposed algorithm in the interferencelimited case. We then address a total power minimization problem with outage specification constraints and its feasibility condition. We propose a dynamic algorithm that adapts the outage probability specification in a heterogeneous network to minimize the total energy consumption and simultaneously guarantees all the femtocell users a minmax fairness in terms of the worst outage probability. Index Terms — Optimization, nonnegative matrix theory, outage probability, power control, femtocell networks.
On the optimality of treating interference as noise
 in Proc. 51st Annu. Allerton Conf. Commun., Control, Comput
, 2013
"... Abstract — It is shown that in the Kuser interference channel, if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in decibel scale), then the simple scheme of usin ..."
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Cited by 9 (4 self)
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Abstract — It is shown that in the Kuser interference channel, if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in decibel scale), then the simple scheme of using pointtopoint Gaussian codebooks with appropriate power levels at each transmitter and treating interference as noise (TIN) at every receiver (in short, TIN scheme) achieves all points in the capacity region to within a constant gap. The generalized degrees of freedom (GDoF) region under this condition is a polyhedron, which is shown to be fully achieved by the same scheme, without the need for timesharing. The results are proved by first deriving a polyhedral relaxation of the GDoF region achieved by TIN, and then providing a dual characterization of this polyhedral region via the use of potential functions, and finally proving the optimality of this region in the desired regime. Index Terms — Capacity region, Gaussian interference channel, generalized degrees of freedom (GDoF), treating interference as noise (TIN). I.
Nonnegative matrix inequalities and their application to nonconvex power control optimization
 SIAM Journal on Matrix Analysis and Applications
"... Abstract. Maximizing the sum rates in a multiuser Gaussian channel by power control is a nonconvex NPhard problem that finds engineering application in code division multiple access (CDMA) wireless communication network. In this paper, we extend and apply several fundamental nonnegative matrix ine ..."
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Cited by 9 (7 self)
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Abstract. Maximizing the sum rates in a multiuser Gaussian channel by power control is a nonconvex NPhard problem that finds engineering application in code division multiple access (CDMA) wireless communication network. In this paper, we extend and apply several fundamental nonnegative matrix inequalities initiated by Friedland and Karlin in a 1975 paper to solve this nonconvex power control optimization problem. Leveraging tools such as the Perron–Frobenius theorem in nonnegative matrix theory, we (1) show that this problem in the power domain can be reformulated as an equivalent convex maximization problem over a closed unbounded convex set in the logarithmic signaltointerferencenoise ratio domain, (2) propose two relaxation techniques that utilize the reformulation problem structure and convexification by Lagrange dual relaxation to compute progressively tight bounds, and (3) propose a global optimization algorithm with ϵsuboptimality to compute the optimal power control allocation. A byproduct of our analysis is the application of Friedland– Karlin inequalities to inverse problems in nonnegative matrix theory.
Crosslayer Optimization for Wireless Networks with Deterministic Channel Models
"... Abstract—Existing work on crosslayer optimization for wireless networks adopts simple physicallayer models, i.e., treating interference as noise. In this paper, we adopt a deterministic channel model proposed in [11, 12], a simple abstraction of the physical layer that effectively captures the eff ..."
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Cited by 8 (4 self)
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Abstract—Existing work on crosslayer optimization for wireless networks adopts simple physicallayer models, i.e., treating interference as noise. In this paper, we adopt a deterministic channel model proposed in [11, 12], a simple abstraction of the physical layer that effectively captures the effect of channel strength, broadcast and superposition in wireless channels. Within the Network Utility Maximization (NUM) framework, we study the crosslayer optimization for wireless networks based on this deterministic channel model. First, we extend the wellapplied conflict graph model to capture the flow interactions over the deterministic channels and characterize the feasible rate region. Then we study distributed algorithms for general wireless multihop networks. The convergence of algorithms is proved by Lyapunov stability theorem and stochastic approximation method. Further, we show the convergence to the bounded neighborhood of optimal solutions with probability one under constant steps and constant update intervals. Our numerical evaluation validates the analytical results. I.
Maximizing Sum Rates in Cognitive Radio Networks: Convex Relaxation and Global Optimization Algorithms
"... Abstract—A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as the sum rates of all the users while avoiding the interference of unlicensed band secondary users from overwhelming the licensed band primary users. We study the weighted sum rate maximiza ..."
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Cited by 6 (3 self)
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Abstract—A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as the sum rates of all the users while avoiding the interference of unlicensed band secondary users from overwhelming the licensed band primary users. We study the weighted sum rate maximization problem with both power budget and interference temperature constraints in a cognitive radio network. This problem is nonconvex and generally hard to solve. We propose a reformulationrelaxation technique that leverages nonnegative matrix theory to first obtain a relaxed problem with nonnegative matrix spectral radius constraints. A useful upper bound on the sum rates is then obtained by solving a convex optimization problem over a closed bounded convex set. It also enables the sumrate optimality to be quantified analytically through the spectrum of speciallycrafted nonnegative matrices. Furthermore, we obtain polynomialtime verifiable sufficient conditions that can identify polynomialtime solvable problem instances, which can be solved by a fixedpoint algorithm. As a byproduct, an interesting optimality equivalence between the nonconvex sum rate problem and the convex maxmin rate problem is established. In the general case, we propose a global optimization algorithm by utilizing our convex relaxation and branchandbound to compute an optimal solution. Our technique exploits the nonnegativity of the physical quantities, e.g., channel parameters, powers and rates, that enables key tools in nonnegative matrix theory such as the (linear and nonlinear) PerronFrobenius theorem, quasiinvertibility, FriedlandKarlin inequalities to be employed naturally. Numerical results are presented to show that our proposed algorithms are theoretically sound and have relatively fast convergence time even for largescale problems. Index Terms—Optimization, convex relaxation, cognitive radio networks, nonnegative matrix theory. I.
Joint downlink base station association and power control for maxmin fairness: Computation and complexity
 IEEE J. Sel. Areas Commun
, 2015
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A Characterization of MaxMin SIRBalanced Power Allocation with Applications
, 2009
"... We consider a powercontrolled wireless network with an established network topology in which the communication links (transmitterreceiver pairs) are corrupted by the cochannel interference and background noise. We have fairly general power constraints since the vector of transmit powers is confin ..."
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Cited by 2 (0 self)
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We consider a powercontrolled wireless network with an established network topology in which the communication links (transmitterreceiver pairs) are corrupted by the cochannel interference and background noise. We have fairly general power constraints since the vector of transmit powers is confined to belong to an arbitrary convex polytope. The interference is completely determined by a socalled gain matrix. Assuming irreducibility of this gain matrix, we provide an elegant characterization of the maxmin SIRbalanced power allocation under such general power constraints. This characterization gives rise to two types of algorithms for computing the maxmin SIRbalanced power allocation. One of the algorithms is a utilitybased power control algorithm to maximize a weighted sum of the utilities of the link SIRs. Our results show how to choose the weight vector and utility function so that the utilitybased solution is equal to the solution of the maxmin SIRbalancing problem. The algorithm is not amenable to distributed implementation as the weights are global variables. In order to mitigate the problem of computing the weight vector in distributed wireless networks, we point out a saddle point characterization of the Perron root of some extended gain matrices and discuss how this characterization can be used in the design of algorithms in which each link iteratively updates its weight vector in parallel to the power control recursion. Finally, the paper provides a basis for the development of distributed power control and beamforming algorithms to find a global solution of the maxmin SIRbalancing problem.