Interference-Aware Selfish Routing in Multi-ratio Multi-channel Wireless Mesh Networks
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Embarrassment
Potential game
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Price of Anarchy
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This paper is concerned with distributed joint user and antenna selection strategy for multiple-input multiple-output broadcast channels. Firstly, the selection is formulated as a discrete game in which the users are regarded as game participants. We design a joint selection strategy and the optimal strategy profile which maximizes the sum rate constitutes a pure strategy Nash equilibrium. Then a distributed algorithm is proposed to achieve the Nash equilibrium. The algorithm is proved to converge on a feasible pure strategy Nash equilibrium under destined conditions. Simulation results show that the proposed algorithm, which only requires limited feedback, can achieve near optimal sum rate performance with lower complexity.
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Transmission investment tends to be increasingly decentralized, and each participant's strategy is interactional in today's power market environment. It is very important for system administrators and each participant to correctly forecast potential investors' actions. This paper analyzes transmission investors' mixed strategy Nash equilibrium based on noncooperative game theory and genetic algorithm.
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Game theory is a well-established discipline in the social sciences that is primarily used for modeling social behavior. Traditionally, the preferences of the individual agents are modeled as utility functions and the resulting behavior is assumed to be an equilibrium concept associated with these modeled utility functions, e.g., Nash equilibrium. This is in stark contrast to the role of game theory in engineering systems where the goal is to design both the agents' utility functions and an adaptation rule such that the resulting global behavior is desirable. The transition of game theory from a modeling tool for social systems to a design tool for engineering systems promotes several new research directions that we will discuss in this talk. In particular, this talk will focus on the question of how to design admissible agent utility functions such that the resulting game possesses desirable properties, e.g., the existence and efficiency of pure Nash equilibria. Our motivation for considering pure Nash equilibria stems from the fact that adaptation rules can frequently be derived which guarantee that the collective behavior will converge to such pure Nash equilibria.
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Game theory is a theory that has been widely used and analyzed in real life circumstances such as trading or investing. It is the study of strategic decision making. This theory can be modeled and applied to mathematics or economy to help people, societies, and governments make better decisions. This paper will discuss the basic concepts of game theory, such as what is game theory, the basic elements of game theory and the practical application of game theory. This paper will elaborate the principles of game theory through the example of rock paper scissors. This paper will also explain the concept of Nash equilibrium, which is a special equilibrium in game theory. This paper will use another example to show under which circumstances Nash equilibrium will be achieved. Through Nash equilibrium theory and case analysis, it is found that rational people will maximize their own interests by looking for a Nash equilibrium in their daily behavior decisions. This indicates that when making some decisions, society and the government need to consider and look for Nash equilibrium as much as possible, so that the whole country can obtain the maximum benefit.
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A novel framework is proposed to introduce the game theory into power control in UWB network. Firstly, the power control problem is modeled as a cooperation potential game. Secondly, a novel utility function and potential function are introduced for UWB network. Finally, the process of power control is expressed as the process of maximum potential function for each active link. For the application of the framework, a new algorithm is also proposed. The algorithm converges to an exact Nash equilibrium by the theoretical proving. Through the simulation which compares the performance of our algorithm and the traditional scheme, the result shows that our algorithm performed better in both convergence and fairness, and also saved power consumed.
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As discussed in this paper, the frequency selective interference channel is important, both from a practical as from an information theoretic point of view. We show that it has many intriguing aspects from a game theoretic point of view as well, and that various levels of interference admit different types of game theoretic techniques.
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Potential game
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A growing body of literature in networked systems research relies on game theory and mechanism design to model and address the potential lack of cooperation between self-interested users. Most game-theoretic models applied to system research only describe competitive equilibria in terms of pure Nash equilibria, that is, a situation where the strategy of each user is deterministic, and is her best response to the strategies of all the other users. However, the assumptions necessary for a pure Nash equilibrium to hold may be too stringent for practical systems. Using three case studies on network formation, computer security, and TCP congestion control, we outline the limits of game-theoretic models relying on Nash equilibria, and we argue that considering competitive equilibria of a more general form helps in assessing the accuracy of a game theoretic model, and can even help in reconciling predictions from game-theoretic models with empirically observed behavior.
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Algorithmic game theory
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We study multi-agent optimization problems described by ordinal potential games. The objective is to reach a Nash equilibrium (NE) in a distributed manner. It is well known that an asynchronous best response dynamics (ABRD) will always converge to a pure NE in this case. However, computing the exact best response at every step of the algorithm may be computationally heavy, if not impossible. Therefore, instead of computing the exact best response, we propose an algorithm that performs a "better response", which decreases the local cost rather than minimizing it. The agents perform a distributed asynchronous gradient descent algorithm, in which only a finite number of iterations of the gradient descent are performed by each player. We prove that this algorithm always converges to a NE and demonstrate via simulations that the computational time to reach the NE can be much shorter than with the classical ABRD. Taking into account the time required for each agent to communicate, the proposed algorithm is shown to also outperform a distributed synchronous gradient descent in simulations.
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Descent (aeronautics)
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Uncoordinated frequency hopping (UFH) has recently emerged as an effective mechanism to defend against jamming attacks. Existing research focuses on the optimal design of the hopping pattern, which implicitly assumes that the strategy of the attacker is fixed. In practice, the attacker might adjust its strategy to maximize its damage on the communication system. In this paper, we study the design of optimal hopping pattern (the defense strategy) as long as the optimal jamming pattern (the attack strategy). In particular, we model the dynamic between the legitimate users and the attacker as a zero sum game, and study the property of this game. We show that when the legitimate users and the jammer can access only one channel at any time, the game has a unique Nash equilibrium. In the Nash equilibrium, the legitimate users and Eve will access or jam only a subset of channels that have good channel quality. Furthermore, the better the channel, the larger the probability that Eve will jam the channel and the smaller the probability the legitimate users will access this channel. We further extend the study to multiple access multiple jamming case and characterize the Nash equilibrium. We also give numerical results to illustrate the analytical results derived in this paper.
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Frequency-hopping spread spectrum
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