EARS: Collaborative Research: Intelligence Measure of Cognitive Radio Networks

EARS：协作研究：认知无线电网络的智能测量

• 批准号: 1443887
• 负责人: Kai Zeng
• 金额: $ 22.51万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443887&HistoricalAwards=false
• 关键词: EARS; Collaborative; Research; Intelligence; Measure

Research on spectrum sharing has generated a large amount of spectrum measurement data and many spectrum sharing techniques. Most of the techniques are based on cognitive radio networks (CRNs) because the cognition capability is necessary for optimizing spectrum efficiency and guaranteeing safe coexistence in the presence of the spectrum uncertainty. Such cognitive capabilities collectively defines the intelligence of CRNs. Although cognition and intelligence are vital for CRNs, their quantitative specification is largely open. This project addresses this open question by developing a framework for investigating quantitatively the cognitive capabilities and the intelligence of CRNs. This project develops both a theoretical approach and an empirical approach to construct a CRN intelligence model. This model is inspired from the Cattell-Horn-Carroll human intelligence model. On the other hand, the pioneering theoretical approach is in sharp contrast to conventional human intelligence research that is mainly empirical. In addition, this project develops a CRN testing battery to measure the intelligence as CRN IQ (intelligence quotient) following psychometric practices. Finally, this project initiates innovative research in IQ-based multi-hop routing and IQ-based immunity to Denial-of-Service attacks, where innovative CRN techniques are developed with the aim of enhancing important cognitive capabilities. This project adopts a big data approach to exploit the vast amount of existing spectrum measurement data and CRN research results. Psychometric techniques are exploited to resolve challenges involved in this big data application.This project impacts society by expediting the commercial success of spectrum sharing technology. It integrates three traditionally disparate areas: wireless communications, psychology, and big data. The framework for constructing intelligence measure is useful to many other systems such as cognitive computing, cognitive control and cognitive radar. This project enhances the value of the spectrum measurement data and leads to a new direction for big data utilization. This project impacts education in many aspects, such as stimulating student research via the human intelligence analogy, supporting hands-on curriculum with the CRN testing battery, setting up CRN IQ competition projects, and outreaching to under-represented students and high school students.This project impacts society by expediting the commercial success of spectrum sharing technology. It integrates three traditionally disparate areas: wireless communications, psychology, and big data. The framework for constructing intelligence measure is useful to many other systems such as cognitive computing, cognitive control and cognitive radar. This project enhances the value of the spectrum measurement data and leads to a new direction for big data utilization. This project impacts education in many aspects, such as stimulating student research via the human intelligence analogy, supporting hands-on curriculum with the CRN testing battery, setting up CRN IQ competition projects, and outreaching to under-represented students and high school students.

对频谱共享的研究已经产生了大量的频谱测量数据和多种频谱共享技术。大多数技术都是基于认知无线电网络(CRN)的，因为认知能力是优化频谱效率和保证在存在频谱不确定性的情况下安全共存所必需的。这些认知能力共同定义了CRN的智能。尽管认知和智力对CRN至关重要，但它们的定量规范在很大程度上是开放的。这个项目通过开发一个定量研究认知网络的认知能力和智力的框架来解决这个悬而未决的问题。本项目采用理论和实证相结合的方法构建CRN智能模型。这个模型的灵感来自卡特尔-霍恩-卡罗尔人类智力模型。另一方面，这种开创性的理论方法与传统的以实证为主的人类智力研究形成了鲜明对比。此外，本项目还开发了CRN测试组件，用于测量遵循心理测量方法的CRN智商(CRN IQ)。最后，该项目启动了基于IQ的多跳路由和基于IQ的拒绝服务攻击免疫力的创新研究，其中创新的CRN技术被开发出来，目的是增强重要的认知能力。本项目采用大数据的方式，充分利用现有的大量频谱测量数据和CRN研究成果。心理测量学技术被用来解决这一大数据应用所涉及的挑战。该项目通过加快频谱共享技术的商业成功来影响社会。它集成了三个传统上截然不同的领域：无线通信、心理学和大数据。该智能测量框架对认知计算、认知控制、认知雷达等其他系统也有一定的参考价值。该项目提升了频谱测量数据的价值，为大数据的利用开辟了新的方向。该项目对教育的影响是多方面的，例如通过人类智能模拟来激发学生的研究，用CRN测试电池支持动手课程，设立CRN智商竞赛项目，以及向代表不足的学生和高中生进行外展。该项目通过加快频谱共享技术的商业成功来影响社会。它集成了三个传统上截然不同的领域：无线通信、心理学和大数据。该智能测量框架对认知计算、认知控制、认知雷达等其他系统也有一定的参考价值。该项目提升了频谱测量数据的价值，为大数据的利用开辟了新的方向。该项目对教育的影响是多方面的，例如通过人类智能类比来激发学生的研究，支持CRN测试电池的实践课程，设立CRN智商竞赛项目，以及向代表不足的学生和高中生进行外展。