The Experts below are selected from a list of 23157 Experts worldwide ranked by ideXlab platform
Ying-chang Liang - One of the best experts on this subject based on the ideXlab platform.
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weighted sum rate Maximization for reconfigurable intelligent surface aided wireless networks
IEEE Transactions on Wireless Communications, 2020Co-Authors: Huayan Guo, Ying-chang 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 RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted Sum-Rate (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 low-complexity 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%.
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weighted sum rate Maximization for intelligent reflecting surface enhanced wireless networks
Global Communications Conference, 2019Co-Authors: Huayan Guo, Ying-chang 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 multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted Sum-Rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex 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 closed-form 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.
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cognitive multiple access channels optimal power allocation for weighted sum rate Maximization
IEEE Transactions on Communications, 2009Co-Authors: Lan Zhang, Ying-chang 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 (C-MAC), 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 C-MAC. 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.
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Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications, 2008Co-Authors: Lan Zhang, Ying-chang Liang, Yan XinAbstract:A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the Sum-Rate Maximization problem and the SINR balancing problem. For the Sum-Rate Maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable Sum-Rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.
Zhiquan Luo - One of the best experts on this subject based on the ideXlab platform.
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a stochastic weighted mmse approach to sum rate Maximization for a mimo interference channel
International Workshop on Signal Processing Advances in Wireless Communications, 2013Co-Authors: Meisam Razaviyayn, Maziar Sanjabi Boroujeni, Zhiquan LuoAbstract:Consider a multiple input-multiple output (MIMO) interference channel with partial channel state information (CSI) whereby the CSI is known only for some (or none) of the links, while the statistical knowledge is known for the remaining links. In this work, we consider the linear transceiver design problem for such an interference channel with partial CSI by maximizing the average long term Sum-Rate of the system. We propose an efficient stochastic Sum-Rate Maximization algorithm based on the iterative optimization of the ensemble average of the sum rate utility function. The proposed algorithm can use the statistical knowledge of the links whenever the actual CSI is not available and is guaranteed to converge to the set of stationary points of the stochastic Sum-Rate Maximization problem almost surely. The effectiveness and the efficiency of the proposed algorithm are validated via numerical experiments.
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an iteratively weighted mmse approach to distributed sum utility Maximization for a mimo interfering broadcast channel
IEEE Transactions on Signal Processing, 2011Co-Authors: Qingjiang Shi, Meisam Razaviyayn, Zhiquan LuoAbstract:Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorithm for weighted Sum-Rate Maximization that is based on iterative minimization of weighted mean-square error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted Sum-Rate Maximization problem. Furthermore, the algorithm and its convergence can be extended to a general class of sum-utility Maximization problem. The effectiveness of the proposed algorithm is validated by numerical experiments.
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an iteratively weighted mmse approach to distributed sum utility Maximization for a mimo interfering broadcast channel
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Qingjiang Shi, Meisam Razaviyayn, Zhiquan LuoAbstract:Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted Sum-Rate Maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted Sum-Rate Maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments.
Erik Larsson - One of the best experts on this subject based on the ideXlab platform.
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weighted sum rate Maximization for reconfigurable intelligent surface aided wireless networks
IEEE Transactions on Wireless Communications, 2020Co-Authors: Huayan Guo, Ying-chang 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 RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted Sum-Rate (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 low-complexity 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%.
Markku Juntti - One of the best experts on this subject based on the ideXlab platform.
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decentralized sum rate Maximization with qos constraints for interfering broadcast channel via successive convex approximation
IEEE Transactions on Signal Processing, 2016Co-Authors: Jarkko Kaleva, Antti Tölli, Markku JunttiAbstract:Weighted sum rate Maximization (WSRMax) with user specific quality-of-service (QoS) constraints and general convex transmit power constraints is considered in multi-cell multi-user multiple-input multiple-output system. The particular focus in the proposed joint transmitter-receiver design is on tractability in terms of implementation and moderately fast changing/time correlated channel conditions. The non-convex transmit precoder design problem is formulated as a difference of convex functions program, for which a locally optimal solution is achieved by successive convex approximation (SCA). To achieve practically realizable designs, two decentralized approaches with low signaling overhead are proposed. Primal decomposition based solution provides better compliance of the provided QoS constraints in slowly fading channel conditions. On the other hand, solution based on Lagrangian relaxation of the coupling rate constraints is proposed for relaxed feasibility conditions and improved convergence properties. As a special case, an iterative solution via the Karush–Kuhn–Tucker conditions of the precoder design problem with per base station transmit power constraints is also proposed. Finally, we propose a heuristic extension of the SCA method, which is shown to significantly improve the rate of convergence while achieving comparable sum rate with recently published methods.
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efficient solutions for weighted sum rate Maximization in multicellular networks with channel uncertainties
IEEE Transactions on Signal Processing, 2013Co-Authors: Muhammad Fainan Hanif, Antti Tölli, Markku Juntti, Lenam Tran, Savo GlisicAbstract:The important problem of weighted sum rate Maximization (WSRM) in a multicellular environment is intrinsically sensitive to channel estimation errors. In this paper, we study ways to maximize the weighted sum rate in a linearly precoded multicellular downlink system where the receivers are equipped with a single antenna. With perfect channel information available at the base stations, we first present a novel fast converging algorithm that solves the WSRM problem. Then, the assumption is relaxed to the case where the error vectors in the channel estimates are assumed to lie in an uncertainty set formed by the intersection of finite ellipsoids. As our main contributions, we present two procedures to solve the intractable nonconvex robust designs based on the worst case principle. The proposed iterative algorithms solve semidefinite programs in each of their steps and provably converge to a locally optimal solution of the robust WSRM problem. The proposed solutions are numerically compared against each other and known approaches in the literature to ascertain their robustness towards channel estimation imperfections. The results clearly indicate the performance gain compared to the case when channel uncertainties are ignored in the design process. For certain scenarios, we also quantify the gap between the proposed approximations and exact solutions.
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weighted sum rate Maximization for mimo broadcast channels using dirty paper coding and zero forcing methods
IEEE Transactions on Communications, 2013Co-Authors: Lenam Tran, Markku Juntti, Mats Bengtsson, Bjorn OtterstenAbstract:We consider precoder design for maximizing the weighted sum rate (WSR) of successive zero-forcing dirty paper coding (SZF-DPC). For this problem, the existing precoder designs often assume a sum power constraint (SPC) and rely on the singular value decomposition (SVD). The SVD-based designs are known to be optimal but require high complexity. We first propose a low-complexity optimal precoder design for SZF-DPC under SPC, using the QR decomposition. Then, we propose an efficient numerical algorithm to find the optimal precoders subject to per-antenna power constraints (PAPCs). To this end, the precoder design for PAPCs is formulated as an optimization problem with a rank constraint on the covariance matrices. A well-known approach to solve this problem is to relax the rank constraints and solve the relaxed problem. Interestingly, for SZF-DPC, we are able to prove that the rank relaxation is tight. Consequently, the optimal precoder design for PAPCs is computed by solving the relaxed problem, for which we propose a customized interior-point method that exhibits a superlinear convergence rate. Two suboptimal precoder designs are also presented and compared to the optimal ones. We also show that the proposed numerical method is applicable for finding the optimal precoders for block diagonalization scheme.
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weighted sum rate Maximization for interfering broadcast channel via successive convex approximation
Global Communications Conference, 2012Co-Authors: Jarkko Kaleva, Antti Tölli, Markku JunttiAbstract:We consider the weighted throughput Maximization with general convex transmit power constraints in multi-cell multi-user multiple-input multiple-output system. The problem formulation is separated into receive beamformer and transmit precoder design problems. The non-convex precoder design problem is reformulated as a difference of convex functions program and solved with successive convex approximation. The convex approximation of the precoder design problem can be further formulated as a second-order cone program. Distributed and iterative solution via Karush-Kuhn-Tucker conditions for the precoder design with sum transmit power constraints is also proposed. It is shown that the rate of convergence can be significantly improved with the proposed algorithm while achieving comparable sum rate when compared to other recently published methods. This is an import factor in practical solutions as this increases the achievable sum rate with respect to required signaling iterations.
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MIMO Downlink Weighted Sum Rate Maximization with Power Constraints per Antenna Groups
2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, 2007Co-Authors: Marian Codreanu, Antti Tölli, Markku Juntti, Matti Latva-ahoAbstract:We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of linear transmit and receive beamformers, according to weighted sum rate Maximization criteria. The proposed method can handle multiple antennas at the BS and at the mobile users with an arbitrary number of data streams per scheduled user. It can also handle a fairly general set of practical power constraints for the transmit beamformers, i.e., we can impose sum power constraints for different subsets of the transmit antennas.
Dongning Guo - One of the best experts on this subject based on the ideXlab platform.
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wireless mimo switching weighted sum mean square error and sum rate optimization
IEEE Transactions on Information Theory, 2013Co-Authors: Fanggang Wang, Xiaojun Yuan, Soung Chang Liew, Dongning GuoAbstract:This paper addresses joint transceiver and relay design for a wireless multiple-input multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multiantenna relay linearly precodes the received (uplink) signals from multiple users and forwards the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on various design criteria is typically nonconvex and difficult to solve. The main contribution of this paper is a unified approach to solve the weighted sum mean square error (MSE) minimization and weighted sum rate Maximization problems in MIMO switching. Specifically, an iterative algorithm is proposed for jointly optimizing the relay's precoder and the users' receive filters to minimize the weighted sum MSE. It is also shown that the weighted sum rate Maximization problem can be reformulated as an iterated weighted sum MSE minimization problem and can, therefore, be solved similarly to the case of weighted sum MSE minimization. With properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high- and low-signal-to-noise-ratio regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.
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wireless mimo switching weighted sum mean square error and sum rate optimization
arXiv: Information Theory, 2012Co-Authors: Fanggang Wang, Xiaojun Yuan, Soung Chang Liew, Dongning GuoAbstract:This paper addresses joint transceiver and relay design for a wireless multiple-input-multiple-output (MIMO) switching scheme that enables data exchange among multiple users. Here, a multi-antenna relay linearly precodes the received (uplink) signals from multiple users before forwarding the signal in the downlink, where the purpose of precoding is to let each user receive its desired signal with interference from other users suppressed. The problem of optimizing the precoder based on various design criteria is typically non-convex and difficult to solve. The main contribution of this paper is a unified approach to solve the weighted sum mean square error (MSE) minimization and weighted sum rate Maximization problems in MIMO switching. Specifically, an iterative algorithm is proposed for jointly optimizing the relay's precoder and the users' receive filters to minimize the weighted sum MSE. It is also shown that the weighted sum rate Maximization problem can be reformulated as an iterated weighted sum MSE minimization problem and can therefore be solved similarly to the case of weighted sum MSE minimization. With properly chosen initial values, the proposed iterative algorithms are asymptotically optimal in both high and low signal-to-noise ratio (SNR) regimes for MIMO switching, either with or without self-interference cancellation (a.k.a., physical-layer network coding). Numerical results show that the optimized MIMO switching scheme based on the proposed algorithms significantly outperforms existing approaches in the literature.
