ECCS - EARS: Collaborative Research: Enhanced Radio Spectrum via Information Acquisition and Learning

点击翻译按钮获取中文摘要

This research focuses on the problem of information acquisition in the context of spectrum sensing and utilization where a (set of) decision maker(s), by carefully controlling a sequence of actions with uncertain outcomes, dynamically refines his/her belief about stochastically time-varying parameters of interest such as spectrum availability and quality, in order to communicate over that spectrum as efficiently as possible.The research represents a new theoretical framework for stochastic learning and decision-making in such settings termed Information Acquisition and Utilization Problems (IAUP). Motivated by a synthesis of the researchers' prior works on adaptive sampling, active hypothesis testing, and restless multi-armed bandits, this framework is particularly apt for problems of spectrum sensing and access for several reasons. First, unlike more general stochastic control frameworks such as partially observable Markov decision problems (POMDP's), the IAUP is a purely informational problem in that the actions of the decision maker change only its information state, but not the state of the underlying environment (spectrum quality). Second, in an IAUP there is a conceptual distinction between two kinds of actions: those taken to obtain/refine the information state, and those taken to utilize the current information state, potentially allowing for tractable solutions in many cases where a separation theorem can be proved between these two sets. Finally, an IAUP can explicitly capture the tradeoff between the cost of spectrum sensing and the accuracy and completeness of the information that can be obtained.