| Summary: | With the advances in telecommunication technologies and dramatic performance enhancement of communication equipments, the communication and computing are converging and autonomous distributed architectures will play more important roles in future wireless communication networks. Therefore, devising distributed and dynamic algorithms for ensuring a robust network operation in time-varying and heterogeneous environments becomes a critical issue. Game theory as a discipline studying the interactions of interdependent autonomous agents provides an ideal framework with a set of mathematical tools for this purpose. In this dissertation, we focus on the use of dynamic games to model, analyze, and design efficient distributed algorithms for the competitive resource management in wireless networks. The motivation for the use of dynamic games is from the consideration of dynamic nature of wireless environment and the wide existence of hierarchical structures in wireless networks modeling. The specific issues addressed in this dissertation are summarized as follows.The first issue is the dynamic network selection in heterogeneous wireless networks with incomplete information. A network selection Bayesian game is formulated for this purpose. In general, the preference (i.e., utility) of a mobile user is private information. Therefore, each user has to make the decision of network selection optimally given only the partial information of the preferences of other users. To study the dynamics of such network selection, the Bayesian best response dynamics and aggregate best response dynamics are applied. Bayesian Nash equilibrium is considered to be the solution of this game, and there is a one-to-one mapping between the Bayesian Nash equilibrium and the equilibrium distribution of the aggregate dynamics. We show that even with incomplete information, the equilibrium of network selection decisions of mobile users can be reached.
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