Fast Algorithms and Performance Bounds for Sum Rate Maximization in Wireless Networks

Tan, Chee Wei; Chiang, Mung; Srikant, R.

doi:10.1109/infcom.2009.5062050

Cited by 76 publications

(118 citation statements)

References 21 publications

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“…Finally, Tan et al [49] have recently obtained interesting results regarding the max-min SIR-balancing problem and a connection to the utility-based approach. Their results, some of which are to a certain extent related to the ones included in this paper, have been obtained independently and presented at the Infocom'09 after the publication of our technical report [44], on which the present paper is fully based.…”

Section: Related Workmentioning

confidence: 99%

A characterization of max–min SIR-balanced power allocation with applications

Stańczak¹,

Kaliszan²,

Bambos

2010

Wireless Netw

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We consider a power-controlled wireless network with an established network topology in which the communication links (transmitter-receiver pairs) are corrupted by the co-channel interference and background noise. We have fairly general power constraints since the vector of transmit powers is confined to belong to an arbitrary convex polytope. The interference is completely determined by a so-called gain matrix. Assuming irreducibility of this gain matrix, we provide an elegant characterization of the maxmin SIR-balanced power allocation under such general power constraints. This characterization gives rise to two types of algorithms for computing the max-min SIR-balanced power allocation. One of the algorithms is a utilitybased power control algorithm to maximize a weighted sum of the utilities of the link SIRs. Our results show how to choose the weight vector and utility function so that the utility-based solution is equal to the solution of the max-min SIR-balancing problem. The algorithm is not amenable to distributed implementation as the weights are global variables. In order to mitigate the problem of computing the weight vector in distributed wireless networks, we point out a saddle point characterization of the Perron root of some extended gain matrices and discuss how this characterization can be used in the design of algorithms in which each link iteratively updates its weight vector in parallel to the power control recursion. Finally, the paper provides a basis for the development of distributed power control and beamforming algorithms to find a global solution of the max-min SIRbalancing problem.

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Section: Related Workmentioning

confidence: 99%

A characterization of max–min SIR-balanced power allocation with applications

Stańczak¹,

Kaliszan²,

Bambos

2010

Wireless Netw

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“…Specifically, we drop the sparse constraint of wm, and solve the traditional uplink problem based on the reduced data model only containing the channels between the selected BSs and users. This problem has been well research [18,19], the readers can go to the references for detail.…”

Section: Problem Formulation and Algorithmmentioning

confidence: 99%

Joint base station selection and beamforming in uplink SIMO networks with per-user power constraints

Jiang

Lin

Shao

2015

2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)

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Consider an uplink SIMO network consisting of multiple base stations (BSs) and multiple users, where each user has an individual transmit power constraint, and the BSs are allowed to cooperate in data receiving. To guarantee fairness among users and avoid heavy burden of backhaul data exchange, we maximize the minimum S-INR based on joint BS selection and beamforming. We formulate this problem from the perspective of sparse beamforming. However, the scaling ambiguity of the receive beamformers will make the sparse constraints trivial. Inspired by the observation that the transmit beamformer is immune to the scaling ambiguity, we apply the duality theorem to the uplink max-min problem under per-user power constraints, and reformulate it as an equivalent downlink problem. An iterative two-stage algorithm is then developed to solve the sparse beamforming problem efficiently. The effectiveness of the proposed algorithm is demonstrated by numerical simulations.Index Terms-Max-min fairness, base station selection, beamforming, uplink-downlink duality, per-user power constraint

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“…This has been indeed an active research area over the last decades [1], [6]- [8]. In particular, different radio resource allocation problems for the MU single-cell setting have been investigated for both single-input single-output (SISO) and MIMO systems considering various design objectives such as power minimization, SR, and utility maximization.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Optimization in Multi-User Multi-Cell Massive MIMO Networks Considering Pilot Contamination

Nguyen

2015

IEEE Access

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In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell (MU-MC) massive multiinput multi-output (MIMO) network. We explicitly consider the pilot contamination effect during the channel estimation in the SEE maximization problem, which aims to optimize the power allocation, number of activated antennas, and pilot assignment. To tackle the SEE maximization problem, we transform it into a subtractive form, which can be solved more efficiently. In particular, we develop an iterative algorithm to solve the transformed problem where optimization of power allocation and number of antennas is performed then pilot assignment optimization is conducted sequentially in each iteration. To tackle the first subproblem, we employ a successive convex approximation (SCA) technique to attain a solvable convex optimization problem. Moreover, we propose a novel iterative low-complexity algorithm based on the Hungarian method to solve the pilot assignment subproblem. We also describe how the proposed solution approach can be useful to address the sum rate (SR) maximization problem. In addition to the algorithmic developments, we characterize the optimal structure of both SEE and SR maximization problems. Numerical studies are conducted to illustrate the convergence of the proposed algorithms, impacts of different parameters on the SR and SEE, and significant performance gains of the proposed solution compared the conventional design.

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Fast Algorithms and Performance Bounds for Sum Rate Maximization in Wireless Networks

Cited by 76 publications

References 21 publications

A characterization of max–min SIR-balanced power allocation with applications

A characterization of max–min SIR-balanced power allocation with applications

Joint base station selection and beamforming in uplink SIMO networks with per-user power constraints

Resource Allocation Optimization in Multi-User Multi-Cell Massive MIMO Networks Considering Pilot Contamination

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