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Decomposition by partial linearization: Parallel optimization of multiuser systems
 IEEE Trans. on Signal Processing
, 2014
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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 22 (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.
Distributed Dynamic Pricing for MIMO Interfering Multiuser Systems: A Unified Approach
"... Abstract—Wireless networks are composed of many users that usually have conflicting objectives and generate interference to each other. The system design is typically formulated as the optimization of the weighted sum of the users ’ utility functions. In an attempt to obtain distributed algorithms i ..."
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Cited by 17 (5 self)
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Abstract—Wireless networks are composed of many users that usually have conflicting objectives and generate interference to each other. The system design is typically formulated as the optimization of the weighted sum of the users ’ utility functions. In an attempt to obtain distributed algorithms in the case this sum is nonconvex, researchers have proposed pricing mechanisms which however are based on heuristics and valid only for a restricted class of problems. In this paper we propose a general framework for the distributed optimization of the nonconvex sumutility function. Our main contributions are: i) the derivation for the first time of a general dynamic pricing mechanism, ii) a framework that can be easily particularized to wellknown applications, giving rise to very efficient practical algorithms that outperform existing methods; and iii) the solution to the currently open problem of social optimization for MIMO multiuser systems. I.
Power control for cognitive radio networks under channel uncertainty
 IEEE Trans. Wireless Commun
, 2011
"... Abstract—Cognitive radio (CR) networks can reuse the RF spectrum licensed to a primary user (PU) network, provided that the interference inflicted to the PUs is carefully controlled. However, due to lack of explicit cooperation between CR and PU systems, it is often difficult for CRs to acquire CR ..."
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Cited by 16 (6 self)
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Abstract—Cognitive radio (CR) networks can reuse the RF spectrum licensed to a primary user (PU) network, provided that the interference inflicted to the PUs is carefully controlled. However, due to lack of explicit cooperation between CR and PU systems, it is often difficult for CRs to acquire CRtoPU channels accurately. In fact, if the PU receivers are off, the sensing algorithms cannot obtain the channels for the PU receivers, although they have to be protected nevertheless. In order to achieve aggressive spectrum reuse even in such challenging scenarios, power control algorithms that take channel uncertainty into account are developed. Both lognormal shadowing and smallscale fading effects are considered through suitable approximations. Accounting for the latter, centralized network utility maximization (NUM) problems are formulated, and their KarushKuhnTucker points are obtained via sequential geometric programming. For the case where CRtoCR channels are also uncertain, a novel outage probabilitybased NUM formulation is proposed, and its solution method developed in a unified fashion. Numerical tests verify the performance merits of the novel design. Index Terms—Cognitive radio, power control, network utility maximization, channel uncertainty, interference modeling, geometric programming. I.
Joint linear precoder optimization and base station selection for an uplink MIMO network: A game theoretic approach
 in the Proceedings of the IEEE ICASSP
, 2012
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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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Duality Gap Estimation and Polynomial Time Approximation for Optimal Spectrum Management
, 2008
"... Consider a communication system whereby multiple users share a common frequency band and must choose their transmit power spectra jointly in response to physical channel conditions including the effects of interference. The goal of the users is to maximize a systemwide utility function (e.g., weigh ..."
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Cited by 11 (1 self)
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Consider a communication system whereby multiple users share a common frequency band and must choose their transmit power spectra jointly in response to physical channel conditions including the effects of interference. The goal of the users is to maximize a systemwide utility function (e.g., weighted sumrate of all users), subject to individual power constraints. A popular approach to solve the discretized version of this nonconvex problem is by Lagrangian dual relaxation. Unfortunately the discretized spectrum management problem is NPhard and its Lagrangian dual is in general not equivalent to the primal formulation due to a positive duality gap. In this paper, we use a convexity result of Lyapunov to estimate the size of duality gap for the discretized spectrum management problem and show that the duality gap vanishes asymptotically at the rate O(1= p N), where N is the size of the uniform discretization of the shared spectrum. If the channels are frequency at, the duality gap estimate improves to O(1=N). Moreover, when restricted to the FDMA spectrum sharing strategies, we show that the Lagrangian dual relaxation, combined with a linear programming scheme, can generate an optimal solution for the continuous formulation of the spectrum management problem in polynomial time for any > 0.
Distributed Opportunistic Scheduling for AdHoc Communications Under Delay Constraints
"... Abstract—With the convergence of multimedia applications and wireless communications, there is an urgent need for developing new scheduling algorithms to support realtime traffic with stringent delay requirements. However, distributed scheduling under delay constraints is not well understood and re ..."
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Cited by 10 (0 self)
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Abstract—With the convergence of multimedia applications and wireless communications, there is an urgent need for developing new scheduling algorithms to support realtime traffic with stringent delay requirements. However, distributed scheduling under delay constraints is not well understood and remains an underexplored area. A main goal of this study is to take some steps in this direction and explore the distributed opportunistic scheduling (DOS) with delay constraints. Consider a network with M links which contend for the channel using random access. Distributed scheduling in such a network requires joint channel probing and distributed scheduling. Using optimal stopping theory, we explore DOS for throughput maximization, under two different types of average delay constraints: 1) a networkwide constraint where the average delay should be no greater than α; or 2) individual user constraints where the average delay per user
An analytical framework for heterogeneous partial feedback design in heterogeneous multicell ofdma networks
 IEEE Transactions on Signal Processing
, 2013
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Distributed Throughput Maximization in Wireless Networks via Random Power Allocation
"... Abstract—We consider throughputoptimal power allocation in multihop wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach t ..."
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Cited by 9 (1 self)
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Abstract—We consider throughputoptimal power allocation in multihop wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach to deal with this difficulty. To this end, we generalize the randomization framework originally proposed for input queued switches to an SINR ratebased interference model. Further, we develop distributed power allocation and comparison algorithms that satisfy these conditions, thereby achieving (nearly) 100% throughput. We illustrate the performance of our proposed power allocation solution through numerical investigation and present several extensions for the considered problem. Index Terms—Power allocation, wireless scheduling, capacity region, graphbased interference model, SINR interference model. I.