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

ECCS - EARS：协作研究：通过信息获取和学习增强无线电频谱

• 批准号: 1248017
• 负责人: Bhaskar Krishnamachari
• 金额: $ 25万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248017&HistoricalAwards=false
• 关键词: ECCS; EARS; Collaborative; Research; Enhanced

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.

本研究集中在频谱感知和利用背景下的信息获取问题，其中（一组）决策者通过仔细控制具有不确定结果的一系列动作，动态地改进他/她对随机时变参数的信念，例如频谱可用性和质量，为了尽可能有效地在频谱上进行通信，这项研究代表了一个新的理论框架，在这种情况下称为信息获取和利用问题（IAUP）的随机学习和决策。由于综合了研究人员先前在自适应采样、主动假设检验和不安分的多臂强盗方面的工作，该框架特别适合于频谱感知和接入问题，原因有几个。首先，不像更一般的随机控制框架，如部分可观测马尔可夫决策问题（POMDP），IAUP是一个纯粹的信息问题，决策者的行为只改变其信息状态，但不改变底层环境（频谱质量）的状态。第二，在IAUP中，两种行为之间存在概念上的区别：获取/细化信息状态的行为，以及利用当前信息状态的行为，在许多情况下，可以证明这两种行为之间的分离定理，从而可能提供易于处理的解决方案。最后，IAUP可以明确地捕获频谱感测的成本与可以获得的信息的准确性和完整性之间的权衡。