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: 1247995
• 负责人: Tara Javidi
• 金额: $ 25万
• 依托单位: University of California-San Diego
• 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 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247995&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.

这项研究重点关注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行动，动态地完善他/她对随机时间的信念改变感兴趣的参数，例如频谱可用性和质量，以便尽可能有效地在该频谱上进行通信。该研究为此类环境中的随机学习和决策提供了一个新的理论框架，称为信息获取和利用问题（IAUP）。在综合研究人员之前关于自适应采样、主动假设检验和不安分多臂老虎机的工作的推动下，该框架特别适合解决频谱感知和访问问题，原因有几个。首先，与更一般的随机控制框架（例如部分可观察马尔可夫决策问题（POMDP））不同，IAUP 是一个纯粹的信息问题，因为决策者的行为仅改变其信息状态，而不改变底层环境（频谱）的状态。质量）。其次，在 IAUP 中，两种行为之间存在概念上的区别：为获取/细化信息状态而采取的行为，以及为利用当前信息状态而采取的行为，在许多情况下可能允许使用分离定理来实现易于处理的解决方案。证明在这两组之间。最后，IAUP 可以明确地捕获频谱感知成本与可获得信息的准确性和完整性之间的权衡。