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.

本研究的重点是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一系列具有不确定结果的行动，动态地改进他/她对随机时变的感兴趣参数（如频谱可用性和质量）的信念，以便在该频谱上尽可能有效地进行通信。该研究为这种环境下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）。由于综合了研究人员先前在自适应采样、主动假设检验和不稳定的多臂强盗方面的工作，该框架特别适用于频谱感知和访问问题，原因有几个。首先，与部分可观察马尔可夫决策问题（POMDP）等更一般的随机控制框架不同，IAUP是一个纯粹的信息问题，因为决策者的行为只改变其信息状态，而不改变底层环境的状态（频谱质量）。其次，在IAUP中，两种操作之间存在概念上的区别：用于获取/改进信息状态的操作，以及用于利用当前信息状态的操作，在许多可以在这两组之间证明分离定理的情况下，潜在地允许可处理的解决方案。最后，IAUP可以明确地捕获频谱感知成本与可以获得的信息的准确性和完整性之间的权衡。