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Coalitional Game Theory for Communication Networks: A Tutorial
 IEEE SIGNAL PROCESSING MAGAZINE
"... Game theoretical techniques have recently become prevalent in many engineering applications, notably in communications. With the emergence of cooperation as a new communication paradigm, and the need for selforganizing, decentralized, and autonomic networks, it has become imperative to seek suitabl ..."
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Game theoretical techniques have recently become prevalent in many engineering applications, notably in communications. With the emergence of cooperation as a new communication paradigm, and the need for selforganizing, decentralized, and autonomic networks, it has become imperative to seek suitable game theoretical tools that allow to analyze and study the behavior and interactions of the nodes in future communication networks. In this context, this tutorial introduces the concepts of cooperative game theory, namely coalitional games, and their potential applications in communication and wireless networks. For this purpose, we classify coalitional games into three categories: Canonical coalitional games, coalition formation games, and coalitional graph games. This new classification represents an applicationoriented approach for understanding and analyzing coalitional games. For each class of coalitional games, we present the fundamental components, introduce the key properties, mathematical techniques, and solution concepts, and describe the methodologies for applying these games in several applications drawn from the stateoftheart research in communications. In a nutshell, this article constitutes a unified treatment of coalitional game theory tailored to the demands of communications and network engineers.
Local TwoStage Myopic Dynamics for Network Formation Games
"... Abstract. Network formation games capture two conflicting objectives of selfinterested nodes in a network. On one hand, such a node wishes to be able to reach all other nodes in the network; on the other hand, it wishes to minimize its cost of participation. We focus on myopic dynamics in a class of ..."
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Abstract. Network formation games capture two conflicting objectives of selfinterested nodes in a network. On one hand, such a node wishes to be able to reach all other nodes in the network; on the other hand, it wishes to minimize its cost of participation. We focus on myopic dynamics in a class of such games inspired by transportation and communication models. A key property of the dynamics we study is that they are local: nodes can only deviate to form links with others in a restricted neighborhood. Despite this locality, we find that our dynamics converge to efficient or nearly efficient outcomes in a range of settings of interest. 1
Network Formation: Neighborhood Structures, Establishment Costs, and Distributed Learning
, 2012
"... We consider the problem of network formation in a distributed fashion. Network formation is modeled as a strategicform game, where agents represent nodes that form and sever unidirectional links with other nodes and derive utilities from these links. Furthermore, agents can form links only with a l ..."
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We consider the problem of network formation in a distributed fashion. Network formation is modeled as a strategicform game, where agents represent nodes that form and sever unidirectional links with other nodes and derive utilities from these links. Furthermore, agents can form links only with a limited set of neighbors. Agents trade off the benefit from links, determined by a distancedependent reward function, and the cost of maintaining links. When each agent acts independently trying to maximize its own utility function, we can characterize “stable” networks through the notion of Nash equilibrium. In fact, the introduced reward and cost functions lead to Nash equilibria (networks) which exhibit several desirable properties such as connectivity, boundedhop diameter and efficiency (i.e., minimum number of links). Since Nash networks may not necessarily be efficient, we also explore the possibility of “shaping ” the set of Nash networks through the introduction of statebased utility functions. Such utility functions may represent dynamic phenomena such as establishment costs (either positive or negative). Finally, we show how Nash networks can be the outcome of a distributed learning process. In particular, we extend previous learning processes to socalled “statebased ” weakly acyclic games and we show that the proposed network formation games belong to this class of games.
Content Distribution in Vehicular Networks using
"... Abstract—The popular content distribution (PCD) problem in Vehicular Adhoc Networks (VANETs) is considered. We model this problem as a coalitional graph game in which the onboard units (OBUs) try to form a peertopeer (P2P) network where one OBU transmits files to multi OBUs but receives from onl ..."
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Abstract—The popular content distribution (PCD) problem in Vehicular Adhoc Networks (VANETs) is considered. We model this problem as a coalitional graph game in which the onboard units (OBUs) try to form a peertopeer (P2P) network where one OBU transmits files to multi OBUs but receives from only one OBU each time, to complete the data dissemination efficiently. In this game, the OBUs engage in bilateral negotiations which result in a bilaterally agreement of forming a directed link among OBUs. Once the network is constructed, the OBUs will transmit the content pieces to each other. We study this game under a form of myopic dynamics which is carried out by each OBU in a distributed way. Furthermore, the network formed in this game is a pairwise stable network. Simulation results show that the proposed approach performs better comparing with the noncooperative case. Keywords—Vehicular Adhoc Networks, popular content distribution, coalitional graph game, myopic, pairwise stable I.
and Mathematical Engineering
"... Abstract—We consider a network formation game where a finite number of nodes wish to send traffic to each other. Nodes contract bilaterally with each other to form communication links; once the network is formed, traffic is routed along shortest paths (if possible). Cost is incurred to a node from f ..."
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Abstract—We consider a network formation game where a finite number of nodes wish to send traffic to each other. Nodes contract bilaterally with each other to form communication links; once the network is formed, traffic is routed along shortest paths (if possible). Cost is incurred to a node from four sources: (1) routing traffic; (2) maintaining links to other nodes; (3) disconnection from destinations the node wishes to reach; and (4) payments made to other nodes. We assume that a network is stable if no single node wishes to unilaterally deviate, and no pair of nodes can profitably deviate together. We characterize stable networks, and study the efficiency of those networks. We also consider myopic best response dynamics in the case where links are bidirectional. Under certain assumptions, these myopic dynamics converge to a stable network; further, they naturally select an efficient equilibrium out of the set of possible equilibria. I.
A Coevolutionary Model of Strategic Network Formation
, 2013
"... In foundational models of network formation, the mechanisms for link formation are based solely on network topology. For example, preferential attachment uses degree distributions, whereas a strategic connections model uses internode distances. These dynamics implicitly presume that such benefits an ..."
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In foundational models of network formation, the mechanisms for link formation are based solely on network topology. For example, preferential attachment uses degree distributions, whereas a strategic connections model uses internode distances. These dynamics implicitly presume that such benefits and costs are instantaneous functions of the network topology. A more detailed model would include that benefits and costs are themselves derived through a dynamic process, which, in the absence of timescale separation, necessitates a coevolutionary analysis. This paper introduces a new coevolutionary model of strategic network formation. In this model, network formation evolves along with the flow of benefits from one node to another. We examine the emergent equilibria of this combined dynamics of network formation and benefit flow. We show that the class of strict equilibria is stable (or robust to small perturbations in the benefits flows). 1
1Network Formation: Neighborhood Structures, Establishment Costs, and Distributed Learning
, 2011
"... We consider the problem of network formation in a distributed fashion. Network formation is modeled as a strategicform game, where agents represent nodes that form and sever unidirectional links with other nodes and derive utilities from these links. Furthermore, agents can form links only with a l ..."
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We consider the problem of network formation in a distributed fashion. Network formation is modeled as a strategicform game, where agents represent nodes that form and sever unidirectional links with other nodes and derive utilities from these links. Furthermore, agents can form links only with a limited set of neighbors. Agents trade off the benefit from links, determined by a distancedependent reward function, and the cost of maintaining links. When each agent acts independently trying to maximize its own utility function, we can characterize “stable” networks through the notion of Nash equilibrium. In fact, the introduced reward and cost functions lead to Nash equilibria (networks) which exhibit several desirable properties such as connectivity, boundedhop diameter and efficiency (i.e., minimum number of links). Since Nash networks may not necessarily be efficient, we also explore the possibility of “shaping ” the set of Nash networks through the introduction of statebased utility functions. Such utility functions may represent dynamic phenomena such as establishment costs (either positive or negative). Finally, we show how Nash networks can be the outcome of a distributed learning process. In particular, we extend previous learning processes to socalled “statebased ” weakly acyclic games and we show that the proposed network formation games belong to this class of games. I.