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.

这项研究的重点是在频谱感应和利用的背景下，通过仔细控制具有不确定结果的一系列动作，动态地完善对频谱可用性和质量等新型参数的信念，以使其能够进行新的范围，从而有效地进行了一些范围，从而使他/她的信念动态地完善了他/她的信念。在这种情况下，随机学习和决策称为信息获取和利用问题（IAUP）。由研究人员在自适应采样，主动假设测试和躁动不安的多武器匪徒方面的综合作品的综合动机中，该框架特别适合出于多种原因而出于多种原因而访问频谱感应和访问问题。首先，与更一般的随机控制框架（例如部分可观察到的马尔可夫决策问题（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，但不能改变基本环境的状态（Spectrum质量）。其次，在IAUP中，两种动作之间存在概念上的区别：为获取/完善信息状态的操作，以及用于利用当前信息状态的那些动作，在许多情况下可以在这两组之间证明分离定理的情况下，有可能允许可拖动的解决方案。最后，IAUP可以明确捕获频谱传感成本与可以获得的信息的准确性和完整性之间的权衡。