Convex-concave procedure for weighted sum-rate maximization in a MIMO interference network

You, Seungil; Chen, Lijun; Liu, Youjian

doi:10.1109/glocom.2014.7037443

Cited by 11 publications

(15 citation statements)

References 21 publications

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“…Indeed, the dual link algorithm is highly scalable and suitable for distributed implementation, which is briefly discussed in this paper. The centralized version of dual link algorithm for total power constraint has been generalized to multiple linear constraints using a minimax approach [14], and has stimulated the design of another monotonic convergent algorithm based on convexconcave procedure [15] which has slower convergence but can handle nonlinear convex constraints. Nevertheless, the dual link algorithm uses a different derivation approach, which is based on the optimal transmit signal structure, and easily leads a low 1 2 3 T X 1 T X 2 R X 1 R X 2 Fig.…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for the weighted sum rate maximization in MIMO interference networks

Xing

You

Chen

et al. 2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

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MIMO interference network optimization is important for increasingly crowded wireless communication networks. This paper presents a new algorithm, named Dual Link Algorithm, for weighted sum-rate maximization where the interference is efficiently managed. We consider general MIMO interference channels with Gaussian input and a total power constraint. Two of the previous state-of-the-art algorithms are the WMMSE algorithm and the polite water-filling (PWF) algorithm. The WMMSE algorithm is provably convergent, while the PWF algorithm takes the advantage of the optimal transmit signal structure and converges the fastest in most situations but is not guaranteed to converge in all situations. Therefore, it is highly desirable to design an algorithm that has the advantages of both algorithms. The proposed dual link algorithm is such an algorithm. Its fast and guaranteed convergence is important to distributed implementation and time varying channels. In addition, the technique and a scaling invariance property used in the convergence proof may find applications in other non-convex problems in communication networks.

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Section: Introductionmentioning

confidence: 99%

A new algorithm for the weighted sum rate maximization in MIMO interference networks

Xing

You

Chen

et al. 2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

Self Cite

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“…To make this possible, we express the involving non-convex constraints as a difference of two convex functions. In fact, the CCP can be seen as a special case of the SCA framework [30] (also known as majorization-minimization (MM)), where a locally tight approximation of the non-convex optimization problem is solved at each iteration [31]. Our contribution in this regard is to develop a second-order cone program (SOCP) in each iteration of the proposed iterative procedure.…”

Section: B Contributionsmentioning

confidence: 99%

“…Obviously, the constraints in (13) are non-convex due to the concave term −|| · || 2 2 . In the light of the CCP, this concave part is linearized to obtain a convex approximation [31]. For the description purpose, let us denote by x (n) the value of an optimization variable x after n iterations of the proposed iterative algorithm described below in Algorithm 1.…”

Section: B Continuous Relaxationmentioning

confidence: 99%

Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks

Sugathapala

Hanif

Lorenzo

et al. 2018

IEEE Trans. Commun.

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Publication informationIEEE Transactions on Communications, 66 (8): [3501][3502][3503][3504][3505][3506][3507][3508][3509][3510][3511][3512][3513][3514][3515][3516] Publisher IEEE Item record/more information http://hdl.handle.net/10197/10385Publisher's statement Abstract-Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users' quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.Index Terms-DNA networks, user association, SOCP, throughput maximization, beamforming, convex optimization, branch and bound algorithm, exhaustive search. This work was presented in part at the workshop on MIMO and cognitive radio technologies in multihop network (MIMOCR), IEEE ICC 2015, June 2015 [1]. I. Sugathapala, S. Glisic, and M. Juntti are with the Centre for Wireless Communications, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland, Emails:{inosha.sugathapala, savo.glisic, markku.juntti}@oulu.fi. B. Lorenzo was with the Centre for wireless Communications, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. She is now with the University of Vigo, Spain, Email: blorenzo@gti.uvigo.es. M. F. Hanif was with the Centre for wireless Communication, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. He has also been with the School of Computing and Communications, Lancaster University, United Kingdom. He is currently associated with the

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“…In the first step, problems (18) and (20) will be convexified by using a linear approximation of the nonconvex terms. This is the approach taken in papers such as [22], [23], and [24]. Instead of solving the reformulated (convex) problem, in the second step, we design a quadratic approximation of the remaining convex terms in order to find a surrogate problem easier to solve.…”

Section: B Weighted Sum-based Formulation To Solve (13)mentioning

confidence: 99%

“…In this appendix, we are going to describe the benchmarks based on the works in [22], [23], and [24]. We start with the benchmark for problem (18).…”

Section: Appendix a Benchmark Formulations And Algorithmsmentioning

confidence: 99%

Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems

Rubio¹,

Pascual‐Iserte²,

Palomar³

et al. 2016

IEEE Trans. Signal Process.

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We present an approach to solve the nonconvex optimization problem that arises when designing the transmit covariance matrices in multiuser multiple-input multiple-output (MIMO) broadcast networks implementing simultaneous wireless information and power transfer (SWIPT). The MIMO SWIPT problem is formulated as a general multi-objective optimization problem, in which data rates and harvested powers are optimized simultaneously. Two different approaches are applied to reformulate the (nonconvex) multi-objective problem. In the first approach, the transmitter can control the specific amount of power to be harvested by power transfer whereas in the second approach the transmitter can only control the proportion of power to be harvested among the different harvesting users. The computational complexity will also be different, with higher computational resources required in the first approach. In order to solve the resulting formulations, we propose to use the majorizationminimization (MM) approach. The idea behind this approach is to obtain a convex function that approximates the nonconvex objective and, then, solve a series of convex subproblems that will converge to a locally optimal solution of the general nonconvex multi-objective problem. The solution obtained from the MM approach is compared to the classical block-diagonalization (BD) strategy, typically used to solve the nonconvex multiuser MIMO network by forcing no interference among users. Simulation results show that the proposed approach improves over the BD approach both the system sum rate and the power harvested by users. Additionally, the computational times needed for convergence of the proposed methods are much lower than the ones required for classical gradient-based approaches.

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Convex-concave procedure for weighted sum-rate maximization in a MIMO interference network

Cited by 11 publications

References 21 publications

A new algorithm for the weighted sum rate maximization in MIMO interference networks

A new algorithm for the weighted sum rate maximization in MIMO interference networks

Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks

Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems

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