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Matti Latvaaho  One of the best experts on this subject based on the ideXlab platform.

maximization of worst case Weighted Sum rate for miso downlink systems with imperfect channel knowledge
IEEE Transactions on Communications, 2015CoAuthors: S Joshi, Marian Codreanu, U L Wijewardhana, Matti LatvaahoAbstract:The problem of robust Weighted Sumrate maximization (WSRMax) in multicell downlink multiinput singleoutput systems is considered. We asSume that channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NPhard even in the case of perfect CSI. We propose optimal and suboptimal but fastconverging algorithms for WSRMax problem with CSI errors. AsSuming bounded ellipsoidal model for the CSI errors, we optimize the worstcase Weighted Sumrate. The proposed optimal algorithm is based on branch and bound (BB) technique, and it globally solves the worstcase WSRMax problem with an optimality certificate. As the convergence speed of the BB method can be slow for large networks, we also provide a fast but possibly suboptimal algorithm based on alternating optimization technique and sequential convex programming. The optimal BB based algorithm can be used to provide performance benchmarks for any suboptimal algorithm. Numerical results show that the convergence speed of the suboptimal algorithm is fast, and it finds a closetooptimal solution in only a few iterations.

maximization of worst case Weighted Sum rate for miso downlink systems with channel uncertainty
International Conference on Communications, 2015CoAuthors: S Joshi, Marian Codreanu, U L Wijewardhana, Matti LatvaahoAbstract:The problem of robust Weighted Sumrate maximization (WSRMax) in multicell downlink multiinput singleoutput systems is considered. We asSume that the channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NPhard even in the case of perfect CSI. AsSuming a bounded ellipsoidal model for the CSI errors, we maximize the worstcase Weighted Sumrate and proposed a fast but possibly suboptimal algorithm. The proposed algorithm is based on alternating optimization technique and sequential convex programming. Numerical results show that the convergence speed of the proposed algorithm is fast, and it finds a closetooptimal solution in only a few iterations.

Weighted Sum rate maximization for full duplex mimo interference channels
IEEE Transactions on Communications, 2015CoAuthors: Ali Cagatay Cirik, Rui Wang, Yingbo Hua, Matti LatvaahoAbstract:We consider a $K$ link multipleinput multipleoutput (MIMO) interference channel, where each link consists of two fullduplex (FD) nodes exchanging information simultaneously in a bidirectional communication fashion. The nodes in each pair suffer from selfinterference due to operating in FD mode, and interuser interference from other links due to simultaneous transmission at each link. We consider the transmit and receive filter design for Weighted Sumrate (WSR) maximization problem subject to Sumpower constraint of the system or individual power constraints at each node of the system. Based on the relationship between WSR and Weighted minimummeansquarederror (WMMSE) problems for FD MIMO interference channels, we propose a low complexity alternating algorithm which converges to a local WSR optimum point. Moreover, we show that the proposed algorithm is not only applicable to FD MIMO interference channels, but also applicable to FD cellular systems in which a base station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) users operating in halfduplex (HD) mode, simultaneously. It is shown in simulations that the Sumrate achieved by FD mode is higher than the Sumrate achieved by baseline HD schemes.

Weighted Sum rate maximization in wireless networks a review
2012CoAuthors: Pradeep Chathuranga Weeraddana, Marian Codreanu, Matti LatvaahoAbstract:The Weighted Sumrate maximization (WSRMax) problem plays a central role in many network control and optimization methods, such as power control, link scheduling, crosslayer control, network utility maximization. The problem is NPhard in general. In Weighted SumRate Maximization in Wireless Networks: A Review, a cohesive discussion of the existing solution methods associated with the WSRMax problem, including global, fast local, as well as decentralized methods is presented. In addition, general optimization approaches, such as branch and bound methods, complementary geometric programming, and decomposition methods, are discussed in depth to address the problem. Through a number of numerical examples, the applicability of the resulting algorithms in various application domains is demonstrated. The presented algorithms and the associated numerical results can be very useful for network engineers or researchers with an interest in network design.

primal decomposition based method for Weighted Sum rate maximization in downlink ofdma systems
Eurasip Journal on Wireless Communications and Networking, 2010CoAuthors: Chathuranga Weeraddana, Marian Codreanu, Matti LatvaahoAbstract:We consider the Weighted Sumrate maximization problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. Motivated by the increasing popularity of OFDMA in future wireless technologies, a low complexity suboptimal resource allocation algorithm is obtained for joint optimization of multiuser subcarrier assignment and power allocation. The algorithm is based on an approximated primal decompositionbased method, which is inspired from exact primal decomposition techniques. The original nonconvex optimization problem is divided into two subproblems which can be solved independently. Numerical results are provided to compare the performance of the proposed algorithm to Lagrange relaxation based suboptimal methods as well as to optimal exhaustive searchbased method. Despite its reduced computational complexity, the proposed algorithm provides closetooptimal performance
Yingchang Liang  One of the best experts on this subject based on the ideXlab platform.

Weighted Sum rate maximization for reconfigurable intelligent surface aided wireless networks
IEEE Transactions on Wireless Communications, 2020CoAuthors: Huayan Guo, Yingchang Liang, Jie Chen, Erik LarssonAbstract:Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RISaided multiuser multipleinput singleoutput (MISO) downlink communication system. Our objective is to maximize the Weighted Sumrate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a lowcomplexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.

Weighted Sum rate maximization for intelligent reflecting surface enhanced wireless networks
Global Communications Conference, 2019CoAuthors: Huayan Guo, Yingchang Liang, Jie Chen, Erik G LarssonAbstract:Intelligent reflecting surface (IRS) is a romising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRSaided multiuser multipleinput singleoutput (MISO) system. A practical IRS asSumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the Weighted Sumrate of all users is maximized by joint optimizing the active beamforming at the basestation (BS) and the passive beamforming at the IRS. This nonconvex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closedform solutions is proposed for the passive beamforming, which is applicable to both the discrete phase shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.

cognitive multiple access channels optimal power allocation for Weighted Sum rate maximization
IEEE Transactions on Communications, 2009CoAuthors: Lan Zhang, Yingchang Liang, Yan Xin, H V PoorAbstract:Cognitive radio is an emerging technology that shows great promise to dramatically improve the efficiency of spectrum utilization. This paper considers a cognitive radio model, in which the secondary network is allowed to use the radio spectrum concurrently with primary users (PUs) provided that interference from the secondary users (SUs) to the PUs is constrained by certain thresholds. The Weighted Sum rate maximization problem is studied under interference power constraints and individual transmit power constraints, for a cognitive multiple access channel (CMAC), in which each SU having a single transmit antenna communicates with the base station having multiple receive antennas. An iterative algorithm is developed to efficiently obtain the optimal solution of the Weighted Sum rate problem for the CMAC. It is further shown that the proposed algorithm, although developed for single channel transmission, can be extended to the case of multiple channel transmission. Corroborating numerical examples illustrate the convergence behavior of the algorithm and present comparisons with other existing alternative algorithms.

Weighted Sum rate optimization for cognitive radio mimo broadcast channels
IEEE Transactions on Wireless Communications, 2009CoAuthors: Lan Zhang, Yan Xin, Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network, in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a Weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs are subject to not only a Sum power constraint but also interference power constraints. We transform this multiconstraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Exploiting the duality result, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are provided to corroborate the effectiveness of the proposed algorithm.

Weighted Sum rate optimization for cognitive radio mimo broadcast channels
International Conference on Communications, 2008CoAuthors: Lan Zhang, Yan Xin, Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network in which the unlicensed (secondary) users (SUs) are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a Weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs have not only the Sum power constraint but also interference constraints. We first transform this multi constraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we establish a duality result for the equivalent problem. Our duality result can be viewed as an extension of the previously known results, which depend on either a Sum power constraint or perantenna power constraints. Furthermore, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Computer simulations are also provided to corroborate the effectiveness of the proposed algorithm.
Marian Codreanu  One of the best experts on this subject based on the ideXlab platform.

maximization of worst case Weighted Sum rate for miso downlink systems with imperfect channel knowledge
IEEE Transactions on Communications, 2015CoAuthors: S Joshi, Marian Codreanu, U L Wijewardhana, Matti LatvaahoAbstract:The problem of robust Weighted Sumrate maximization (WSRMax) in multicell downlink multiinput singleoutput systems is considered. We asSume that channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NPhard even in the case of perfect CSI. We propose optimal and suboptimal but fastconverging algorithms for WSRMax problem with CSI errors. AsSuming bounded ellipsoidal model for the CSI errors, we optimize the worstcase Weighted Sumrate. The proposed optimal algorithm is based on branch and bound (BB) technique, and it globally solves the worstcase WSRMax problem with an optimality certificate. As the convergence speed of the BB method can be slow for large networks, we also provide a fast but possibly suboptimal algorithm based on alternating optimization technique and sequential convex programming. The optimal BB based algorithm can be used to provide performance benchmarks for any suboptimal algorithm. Numerical results show that the convergence speed of the suboptimal algorithm is fast, and it finds a closetooptimal solution in only a few iterations.

maximization of worst case Weighted Sum rate for miso downlink systems with channel uncertainty
International Conference on Communications, 2015CoAuthors: S Joshi, Marian Codreanu, U L Wijewardhana, Matti LatvaahoAbstract:The problem of robust Weighted Sumrate maximization (WSRMax) in multicell downlink multiinput singleoutput systems is considered. We asSume that the channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NPhard even in the case of perfect CSI. AsSuming a bounded ellipsoidal model for the CSI errors, we maximize the worstcase Weighted Sumrate and proposed a fast but possibly suboptimal algorithm. The proposed algorithm is based on alternating optimization technique and sequential convex programming. Numerical results show that the convergence speed of the proposed algorithm is fast, and it finds a closetooptimal solution in only a few iterations.

Worstcase Weighted Sumrate maximization for MISO downlink systems with imperfect channel knowledge
2013 Asilomar Conference on Signals Systems and Computers, 2013CoAuthors: U L Wijewardhana, Marian Codreanu, S Joshi, M. LatvaahoAbstract:We consider the worstcase Weighted Sumrate maximization (WSRMax) problem under imperfect channel state information (CSI) in multicell downlink multipleinput singleoutput systems. The problem is known to be NPhard even for the perfect CSI case. We propose a solution method, based on semidefinite relaxation (SDR) and branch and bound technique, which solves globally the noncovex robust WSRMax problem with an optimality certificate. Novel bounding technique based on SDR is proposed.

Weighted Sum rate maximization in wireless networks a review
2012CoAuthors: Pradeep Chathuranga Weeraddana, Marian Codreanu, Matti LatvaahoAbstract:The Weighted Sumrate maximization (WSRMax) problem plays a central role in many network control and optimization methods, such as power control, link scheduling, crosslayer control, network utility maximization. The problem is NPhard in general. In Weighted SumRate Maximization in Wireless Networks: A Review, a cohesive discussion of the existing solution methods associated with the WSRMax problem, including global, fast local, as well as decentralized methods is presented. In addition, general optimization approaches, such as branch and bound methods, complementary geometric programming, and decomposition methods, are discussed in depth to address the problem. Through a number of numerical examples, the applicability of the resulting algorithms in various application domains is demonstrated. The presented algorithms and the associated numerical results can be very useful for network engineers or researchers with an interest in network design.

primal decomposition based method for Weighted Sum rate maximization in downlink ofdma systems
Eurasip Journal on Wireless Communications and Networking, 2010CoAuthors: Chathuranga Weeraddana, Marian Codreanu, Matti LatvaahoAbstract:We consider the Weighted Sumrate maximization problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. Motivated by the increasing popularity of OFDMA in future wireless technologies, a low complexity suboptimal resource allocation algorithm is obtained for joint optimization of multiuser subcarrier assignment and power allocation. The algorithm is based on an approximated primal decompositionbased method, which is inspired from exact primal decomposition techniques. The original nonconvex optimization problem is divided into two subproblems which can be solved independently. Numerical results are provided to compare the performance of the proposed algorithm to Lagrange relaxation based suboptimal methods as well as to optimal exhaustive searchbased method. Despite its reduced computational complexity, the proposed algorithm provides closetooptimal performance
Olivier Ladislas De Weck  One of the best experts on this subject based on the ideXlab platform.

Adaptive Weighted Sum method for multiobjective optimization: a new method for Pareto front generation
Structural and Multidisciplinary Optimization, 2006CoAuthors: Olivier Ladislas De WeckAbstract:This paper presents an adaptive Weighted Sum (AWS) method for multiobjective optimization problems. The method extends the previously developed biobjective AWS method to problems with more than two objective functions. In the first phase, the usual Weighted Sum method is performed to approximate the Pareto surface quickly, and a mesh of Pareto front patches is identified. Each Pareto front patch is then refined by imposing additional equality constraints that connect the pseudonadir point and the expected Pareto optimal solutions on a piecewise planar hypersurface in the $$ {m} $$ dimensional objective space. It is demonstrated that the method produces a welldistributed Pareto front mesh for effective visualization, and that it finds solutions in nonconvex regions. Two numerical examples and a simple structural optimization problem are solved as case studies.

Adaptive WeightedSum method for biobjective optimization: Pareto front generation
Structural and Multidisciplinary Optimization, 2005CoAuthors: I.y. Kim, Olivier Ladislas De WeckAbstract:This paper presents a new method that effectively determines a Pareto front for biobjective optimization with potential application to multiple objectives. A traditional method for multiobjective optimization is the WeightedSum method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this method often produces poorly distributed solutions along a Pareto front, and that it does not find Pareto optimal solutions in nonconvex regions. The proposed adaptive Weighted Sum method focuses on unexplored regions by changing the weights adaptively rather than by using a priori weight selections and by specifying additional inequality constraints. It is demonstrated that the adaptive Weighted Sum method produces welldistributed solutions, finds Pareto optimal solutions in nonconvex regions, and neglects nonPareto optimal solutions. This last point can be a potential liability of Normal Boundary Intersection, an otherwise successful multiobjective method, which is mainly caused by its reliance on equality constraints. The promise of this robust algorithm is demonstrated with two numerical examples and a simple structural optimization problem.
Lan Zhang  One of the best experts on this subject based on the ideXlab platform.

cognitive multiple access channels optimal power allocation for Weighted Sum rate maximization
IEEE Transactions on Communications, 2009CoAuthors: Lan Zhang, Yingchang Liang, Yan Xin, H V PoorAbstract:Cognitive radio is an emerging technology that shows great promise to dramatically improve the efficiency of spectrum utilization. This paper considers a cognitive radio model, in which the secondary network is allowed to use the radio spectrum concurrently with primary users (PUs) provided that interference from the secondary users (SUs) to the PUs is constrained by certain thresholds. The Weighted Sum rate maximization problem is studied under interference power constraints and individual transmit power constraints, for a cognitive multiple access channel (CMAC), in which each SU having a single transmit antenna communicates with the base station having multiple receive antennas. An iterative algorithm is developed to efficiently obtain the optimal solution of the Weighted Sum rate problem for the CMAC. It is further shown that the proposed algorithm, although developed for single channel transmission, can be extended to the case of multiple channel transmission. Corroborating numerical examples illustrate the convergence behavior of the algorithm and present comparisons with other existing alternative algorithms.

Weighted Sum rate optimization for cognitive radio mimo broadcast channels
IEEE Transactions on Wireless Communications, 2009CoAuthors: Lan Zhang, Yan Xin, Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network, in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a Weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs are subject to not only a Sum power constraint but also interference power constraints. We transform this multiconstraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Exploiting the duality result, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are provided to corroborate the effectiveness of the proposed algorithm.

Weighted Sum rate optimization for cognitive radio mimo broadcast channels
International Conference on Communications, 2008CoAuthors: Lan Zhang, Yan Xin, Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network in which the unlicensed (secondary) users (SUs) are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a Weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs have not only the Sum power constraint but also interference constraints. We first transform this multi constraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we establish a duality result for the equivalent problem. Our duality result can be viewed as an extension of the previously known results, which depend on either a Sum power constraint or perantenna power constraints. Furthermore, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Computer simulations are also provided to corroborate the effectiveness of the proposed algorithm.

Weighted Sum rate optimization for cognitive radio mimo broadcast channels
arXiv: Information Theory, 2008CoAuthors: Lan Zhang, Yan Xin, Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a Weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs have not only the Sum power constraint but also interference constraints. We first transform this multiconstraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Our duality result can solve the optimization problem for MIMOBC with multiple linear constraints, and thus can be viewed as an extension of the conventional results, which rely crucially on a single Sum power constraint. Furthermore, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are further provided to corroborate the effectiveness of the proposed algorithm.