Weighted sum-rate maximization for MISO downlink cellular networks via branch and bound

Joshi,; Weeraddana,; Codreanu, Marian; Latva-aho,

doi:10.1109/acssc.2011.6190283

Cited by 20 publications

(53 citation statements)

References 6 publications

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“…2b, we compare the average sum-rate (w km =1) performances for various precoding strategies as a function of per BS transmit power. The suboptimal solutions achieved 13 by SPCA-WSRM algorithm and other techniques such as AM and WMMSE are indeed very close to the optimal precoding performance obtained from [3]. However, AM and WMMSE require a large number of iterations to reach their respective suboptimal levels.…”

Section: Numerical Resultsmentioning

confidence: 68%

“…2 the authors of [3] numerically prove that the performances of the suboptimal beamforming techniques that achieve the necessary optimality conditions are indeed very close to optimal beamforming design.…”

Section: Arxiv:170804750v1 [Csit] 16 Aug 2017mentioning

confidence: 92%

“…Though the beamforming design methods presented in [3], [4] achieve optimum capacity, these methods may be practically inapplicable since the complexity evolves exponentially with the optimization problem size. Therefore, computationally inexpensive suboptimal beamforming design is very appealing.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Linear Precoding in Downlink Multicell MU-MISO OFDMA Networks

Kibria

Murata

Yoshida

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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Section: Numerical Resultsmentioning

confidence: 68%

Section: Arxiv:170804750v1 [Csit] 16 Aug 2017mentioning

confidence: 92%

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Coordinated Linear Precoding in Downlink Multicell MU-MISO OFDMA Networks

Kibria

Murata

Yoshida

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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“…Specifically, all possibilities of selection variables s ij 's (which are K N in number) that satisfy (7d) and (7e) are considered. For each case, the resulting problem becomes the sum rate maximization for which global optimization methods presented in [39], [40] can be used to find an optimal solution. Then the users-APs assignment combination, which produces the best sum rate, is retained as the global solution.…”

Section: A Globally Optimal Solution By Exhaustive Searchmentioning

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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“…However, for a downlink beamforming system, the WSRM problem is known to be NP-hard [1], therefore, very difficult to find the solution. As a result, we have to be reliant on the centralized and computationally very expensive global optimization approaches [2]- [4] for obtaining the exact solution. However, for a centralized processing based WSRM optimization [5]- [7], the overhead for information exchange among the associated base stations (BSs) may be too massive to be implemented in practical systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed weighted sum-rate maximization in multicell MU-MIMO OFDMA downlink

Kibria

Murata

Zheng

2014

2014 IEEE International Conference on Communications (ICC)

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This paper considers distributed linear beamforming in downlink multicell multiuser orthogonal frequency-division multiple access networks. A fast convergent solution maximizing the weighted sumrate with per base station (BS) transmiting power constraint is formulated. We approximate the nonconvex weighted sum-rate maximization (WSRM) problem with a semidefinite relaxed solvable convex form by means of a series of approximation based on interference alignment (IA) analysis. The WSRM optimization is a two-stage optimization process. In the first stage, the IA conditions are satisfied. In the second stage, the convex approximation of the non-convex WSRM is obtained based on the consequences of IA, and high signal-to-interference-plus-noise ratio assumption. Compared to the conventional iterative distributed algorithms where the BSs exchange additional information at each iteration, the BSs of our proposed solution optimize their beamformers locally without reporting additional information during the iterative procedure.

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Weighted sum-rate maximization for MISO downlink cellular networks via branch and bound

Cited by 20 publications

References 6 publications

Coordinated Linear Precoding in Downlink Multicell MU-MISO OFDMA Networks

Coordinated Linear Precoding in Downlink Multicell MU-MISO OFDMA Networks

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

Distributed weighted sum-rate maximization in multicell MU-MIMO OFDMA downlink

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