Cooperative Strategies for Energy-Aware Ad Hoc Networks: A Correlated-Equilibrium Game-Theoretical Approach

Energy efficiency is a crucial requirement for energy-aware ad hoc networks. Cooperative communications can be applied to significantly reduce and balance energy consumption. However, this advantage mainly depends on efficient cooperative strategies, i.e., the joint issue of relay node selection and...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2013-06, Vol.62 (5), p.2303-2314
Main Authors: Wu, Dan, Cai, Yueming, Zhou, Liang, Zheng, Zhongming, Zheng, Baoyu
Format: Article
Language:English
Subjects:
Ad hoc networks
Applied sciences
Business and industry local networks
Connection and protection apparatus
cooperative communication
correlated equilibrium (CE)
Electrical engineering. Electrical power engineering
Electrical machines
Electrical power engineering
energy efficiency
Exact sciences and technology
Games
Joints
Linear programming
Networks and services in france and abroad
Operation. Load control. Reliability
Pareto optimization
Power networks and lines
Regulation and control
Relays
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
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Summary:Energy efficiency is a crucial requirement for energy-aware ad hoc networks. Cooperative communications can be applied to significantly reduce and balance energy consumption. However, this advantage mainly depends on efficient cooperative strategies, i.e., the joint issue of relay node selection and transmit power control. In this paper, we propose a cooperative behavior control scheme, which determines cooperative strategies by using the correlated equilibrium (CE). Specifically, we first model a cooperative behavior control game where the individual utility function is derived from the energy efficiency in terms of the global max-min fairness with the outage performance constraint. Since the CE can achieve better performance by helping the noncooperative players to coordinate their strategies, we employ the CE to analyze the proposed game. Next, we derive the condition under which the CE is Pareto optimal and employ linear programming duality to show its closed-form expression. Furthermore, we design a linear programming method and a distributed learning algorithm based on the regret matching procedure to achieve the CE, respectively. Simulation results demonstrate that our scheme achieves good convergence, Pareto optimality, and max-min fairness.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2012.2235189