Knowledge and Strategic Learning in Multi-user Communications

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Multi-user wireless communications systems form competitive environments, where heterogeneous and self-interested users compete for the limited spectrum resources. However, the techniques that have recently dominated multi-user communication research are not well suited for heterogeneous environments, because they usually assume transceivers that have similar standards and passively select their actions based on either complete or no knowledge about the competitors? protocols, utilities etc. Such passive system designs do not take advantage of the users? ?smartness? and may lead to inefficient spectrum usage. In contrast, this research characterizes and constructs multi-user communications systems, where users engage in proactive interactions for dividing the spectrum. A new multi-user communication paradigm is proposed, where the interaction between users and their resulting performance is driven not only by their ability to adapt their communication strategies, but also by their ability to make optimal decisions about information exchanges based on their knowledge about their competitors and the environment. The research has two main research thrusts. First, the investigators determine ?the value of knowledge?, which are the performance bounds that can be attained, when users and resource moderators with different amounts of knowledge about the entire communication system and the competing users interact. Moreover, how strategic users should proactively accumulate knowledge and improve their utility is also investigated. Second, the investigators construct operational algorithms that can approach these performance bounds, by systematically acquiring information about other users and deploying strategic learning solutions that enable them to forecast the other users? responses and, ultimately, to optimize their transmission actions. The ?values of learning?, which capture the performance gains for various strategic learning techniques requiring various information overheads and complexity costs, are also quantified.