Decentralized Knowledge and Learning in Strategic Multi-user Communication

A. Motivation Multi-user wireless communications systems form competitive environments, where heterogeneous and strategic users compete for the available spectrum resources. These heterogeneous devices may differ in terms of their adopted standards and architectures, their deployed communication algorithms, their experienced environment (e.g. channel conditions and traffic characteristics), their application-defined utilities etc. [1]. Moreover, these devices can also significantly differ in their ability to sense the environment and acquire information about other users sharing the same spectrum, exchange information and negotiate spectrum access rules with the competing users and, ultimately, learn and reason based on the available information to select their optimal transmission strategies. Importantly, in most communication scenarios, users compete for resources in a strategic manner, i.e. they aim at maximizing their own utilities. The majority of past multi-user communications research has focused on analyzing and quantifying the performance of various multi-user environments, where transceivers with similar standards, algorithms or utilities share the available spectrum resources. An underlying assumption has been that the users select their transmission actions based on either complete or no knowledge about their competitors’ protocols, strategies, utility functions, environment etc. In this report, the term “knowledge” represents the correct beliefs 1 of users about their opponents and the