A Unified Energy Efficiency and Spectral Efficiency Tradeoff Metric in Wireless Networks

Deng, Lei; Rui, Yun; Cheng, Peng; Zhang, Jun; Zhang, Q. T.; Li, Mingqi

doi:10.1109/lcomm.2012.112812.121744

Cited by 94 publications

(47 citation statements)

References 9 publications

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“…More specifically, we can regard (w i , 1−w i ) as a priori articulation of preferences for SE and EE, which captures EE-SE tradeoff in a unified way [13]. Second, we consider the fairness among all N users.…”

Section: B Problem Formulationmentioning

confidence: 99%

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Utility maximization for uplink MU-MIMO: Combining spectral-energy efficiency and fairness

Deng

Zhang

Rui

et al. 2016

2016 European Conference on Networks and Communications (EuCNC)

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Abstract-Driven by green communications, energy efficiency (EE) has become a new important criterion for designing wireless communication systems. However, high EE often leads to low spectral efficiency (SE), which spurs the research on EE-SE tradeoff. In this paper, we focus on how to maximize the utility in physical layer for an uplink multi-user multiple-input multipleoutput (MU-MIMO) system, where we will not only consider EE-SE tradeoff in a unified way, but also ensure user fairness. We first formulate the utility maximization problem, but it turns out to be non-convex. By exploiting the structure of this problem, we find a convexization procedure to convert the original nonconvex problem into an equivalent convex problem, which has the same global optimum with the original problem. Following the convexization procedure, we present a centralized algorithm to solve the utility maximization problem, but it requires the global information of all users. Thus we propose a primal-dual distributed algorithm which does not need global information and just consumes a small amount of overhead. Furthermore, we have proved that the distributed algorithm can converge to the global optimum. Finally, the numerical results show that our approach can both capture user diversity for EE-SE tradeoff and ensure user fairness, and they also validate the effectiveness of our primal-dual distributed algorithm.

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Section: B Problem Formulationmentioning

confidence: 99%

“…• We construct a utility function of all users which not only captures the user diversity for EE-SE tradeoff in a unified way, similar to [13], but also guarantees fairness among all users. Then we maximize the utility function.…”

Section: Introductionmentioning

confidence: 99%

Utility maximization for uplink MU-MIMO: Combining spectral-energy efficiency and fairness

Deng

Zhang

Rui

et al. 2016

2016 European Conference on Networks and Communications (EuCNC)

Self Cite

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show abstract

“…Similar to [4], we utilize the ratio of bandwidth budget and power budget as the unit normalizer for frame SE and frame EE, which is modeled as…”

Section: Ee-se Adaptive Tradeoff Metricmentioning

confidence: 99%

“…By utilizing a transformation and a two-part linearization method, a sequence of traditional programming relaxations of the initial nonconvex programming problem can be derived which are embedded in a branch-and-bound algorithm. In addition, we can also solve the offline problem (13) by using Lagrange dual decomposition as in [4], where the problem is transformed to the dual problem with the given energy consumption of BS. Then the dual problem can be solved iteratively where the BS solves L subproblems as inner loop in parallel and solves the master problem as outer loop with the gradient method.…”

Section: The Problem Presentationmentioning

confidence: 99%

“…The relationship of EE and SE has been characterized firstly in various scenarios, such as the point-to-point link [1] and the heterogeneous networks [2]. Then the tradeoff frameworks of EE and SE are established based on the multi-objective optimization method [3][4], which is mainly to improve the resource utilization efficiency of energy and spectrum. Furthermore, one metric of EE or SE is also maximized with the constraint of another metric [5][6].…”

Section: Introductionmentioning

confidence: 99%

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