Energy-Efficient Resource Allocation in Wireless Networks

Meshkati, Farhad; Poor, H. Vincent; Schwartz, S.C.

doi:10.1109/msp.2007.361602

Cited by 265 publications

(198 citation statements)

References 49 publications

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“…it represents the number of successful bit transmissions that can be made for each Joule of energy drained from the battery. The utility function (4) is widely accepted and indeed it has been already used in a number of previous studies such as [7]- [12]. Of course, there are also alternative choices that could be made.…”

Section: System Model and Problem Statementmentioning

confidence: 99%

“…While the above papers consider the issue of power control assuming that a conventional matched filter is available at the receiver, the recent paper [10] considers the cross-layer problem of joint linear receiver design and power control so as to maximize the utility of each user: it is thus shown in [10] that the inclusion of receiver design in the considered game brings remarkable advantages. This same utility function is also used in [11] for energy-efficient power control in ultra-wideband (UWB) communications, while the survey paper [12] reviews recent advances in the application of a game-theoretic framework for energy-efficient resource allocation.…”

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confidence: 99%

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Joint Receiver and Transmitter Optimization for Energy-Efficient CDMA Communications

Buzzi

Poor

2008

IEEE J. Select. Areas Commun.

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Abstract-This paper focuses on the cross-layer issue of joint multiuser detection and resource allocation for energy efficiency in wireless CDMA networks. In particular, assuming that a linear multiuser detector is adopted in the uplink receiver, the case considered is that in which each terminal is allowed to vary its transmit power, spreading code, and uplink receiver in order to maximize its own utility, which is defined as the ratio of data throughput to transmit power. Resorting to a game-theoretic formulation, a non-cooperative game for utility maximization is formulated, and it is proved that a unique Nash equilibrium exists, which, under certain conditions, is also Paretooptimal. Theoretical results concerning the relationship between the problems of SINR maximization and MSE minimization are given, and, resorting to the tools of large system analysis, a new distributed power control algorithm is implemented, based on very little prior information about the user of interest. The utility profile achieved by the active users in a large CDMA system is also computed, and, moreover, the centralized socially optimum solution is analyzed. Considerations on the extension of the proposed framework to a multi-cell scenario are also briefly detailed. Simulation results confirm that the proposed noncooperative game largely outperforms competing alternatives, and that it exhibits a quite small performance loss with respect to the socially optimum solution, and only in the case in which the users number exceeds the processing gain. Finally, results also show an excellent agreement between the theoretical closedform formulas based on large system analysis and the outcome of numerical experiments.

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Section: System Model and Problem Statementmentioning

confidence: 99%

mentioning

confidence: 99%

Joint Receiver and Transmitter Optimization for Energy-Efficient CDMA Communications

Buzzi

Poor

2008

IEEE J. Select. Areas Commun.

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“…On the other hand, the users that code at the safe rate will have a payoff of r 0 , regardless of the system state. In this case, r 0 is more than the expected average payoff, so σ will grow, as predicted by equation (7). By equation (8), the proportion of users that code at capacity will shrink.…”

Section: Evolutionary Game Theorymentioning

confidence: 96%

“…The strategies of the users then consist in choosing their communication rates, and the receiver's only role is to decode, if possible. Previous work investigated the behavior of users in CDMA ( [4], [5], [6] and [7] just to name a few). In this case the rate is calculated directly from the SINR, and the strategy of a user consists in choosing the transmit power.…”

Section: Introductionmentioning

confidence: 99%

Game theoretic considerations for the Gaussian Multiple Access Channel

Gajic

Rimoldi

2008

2008 IEEE International Symposium on Information Theory

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Abstract-We study the behavior of users in a classical Additive White Gaussian Noise Multiple Access Channel. We model users as rational entities whose only interest is to maximize their own communication rate, and we model their interaction as a noncooperative one-shot game. The Nash equilibria of the two-user game are found, and the relation between the purestrategy and mixed-strategy Nash equilibria is discussed. As in most games, the absence of cooperation and coordination leads to inefficiencies. We then extend our setting using evolutionary game theory, which we use to model a large population of users playing the MAC game over time. A unique evolutionary stable strategy is found for this case, corresponding to the strategy achieving the Nash equilibrium in a simplified one-shot game. Finally, we investigate what happens to the distribution of strategies in a population when we assume that the number of offsprings of a user is equal to the payoff of this user in a one-shot game. We find that the system converges to a state in which the average strategy of the population is the evolutionary stable strategy.

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“…The power control game in wireless networks is a typical non-cooperative game where each mobile decides about his transmit power in order to optimize its performance. This application of non-cooperative game theory and tools is studied in several articles [6,9,7]. The main difference in this article is the use of the evolutionary game theory which deals with population dynamics that is well adapted for studying the power control game in dense wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Power Control Games in Wireless Networks

Altman

El-Azouzi

Hayel

et al. 2008

NETWORKING 2008 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet

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Abstract. In this paper, we apply evolutionary games to non-cooperative power control in wireless networks. Specifically, we focus our study in a power control in W-CDMA and WIMAX wireless systems. We study competitive power control within a large population of mobiles that interfere with each other through many local interactions. Each local interaction involves a random number of mobiles. An utility function is introduced as the difference between a utility function based on SIR of the mobile and pricing. The games are not necessarily reciprocal as the set of mobiles causing interference to a given mobile may differ from the set of those suffering from its interference. We show how the evolution dynamics and the equilibrium behavior (called Evolutionary Stable Strategy -ESS) are influenced by the characteristics of the wireless channel and pricing characteristics.

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Energy-Efficient Resource Allocation in Wireless Networks

Cited by 265 publications

References 49 publications

Joint Receiver and Transmitter Optimization for Energy-Efficient CDMA Communications

Joint Receiver and Transmitter Optimization for Energy-Efficient CDMA Communications

Game theoretic considerations for the Gaussian Multiple Access Channel

Evolutionary Power Control Games in Wireless Networks

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