CCF: Small: Online Learning and Exploitation of the Radio Frequency Spectrum with Sub-Nyquist Sampling

CCF：小型：采用亚奈奎斯特采样的射频频谱在线学习和利用

• 批准号: 1534957
• 负责人: Anna Scaglione
• 金额: $ 40.5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534957&HistoricalAwards=false
• 关键词: CCF; Small; Online; Learning; Exploitation

This project will close technical gaps to enable cognitive radio receivers to explore the radio frequency spectrum online, using the most advanced form of Analog to Digital conversion, referred to as Finite Rate of Innovation (FRI) sampling coupled with the most advanced learning techniques. We plan to use the well-established framework of the multi-armed bandit (MAB) problem, which models the situation of a cognitive radio agent that simultaneously attempts to acquire new knowledge and to optimize its decisions based on what it has previously learned. Our main contribution lies in combining this framework with this novel Analog to Digital receiver front-end, sampling rate below the so called Nyquist limit, adaptively tuning parameters in the sampling structure to sense spectrum opportunities over a much wider range of frequencies than was previously considered possible, and specifically further below what is attainable myopically, without adaptation. The outcome of our study is a cohesive system model for a cognitive sensors, endowed with a decision engine that can optimize not only what to sample but also how to sample analog signals, leveraging on its expected success in finding spectrum holes. The project will explore the complexity of the overall architecture and, ultimately, evaluate the potential benefits of a cognitive MAB-FRI receiver. By moving learning algorithms a step closer to manage directly the data-acquisition interface to the physical world, the research as broad implications in a variety of related sensing problems. The project will also include activities to engage students in classrooms presenting the basic mathematical tools used in this research and minorities in research projects that contribute to advance the broad field of adaptive systems.

该项目将缩小技术差距，以使认知无线电接收器在线探索射频频谱，使用最先进的模拟形式到数字转换，被称为有限的创新率（FRI）采样，再加上最先进的学习技术。我们计划使用多军强盗（MAB）问题的框架框架，该框架对认知无线电代理的状况进行了建模，该框架同时尝试根据以前学到的知识来获取新知识并优化其决策。我们的主要贡献在于将该框架与数字接收器前端的新型类似物相结合，低于所谓的奈奎斯特极限的采样率，在采样结构中适应性调谐参数，以在频率范围内的频率范围较大的频率范围比以前可能的频率范围较大，具体是在不可能的情况下，没有适应性的近视，而没有适应性。 我们研究的结果是一个具有凝聚力的系统模型，用于认知传感器，并具有决策引擎，该决策引擎不仅可以优化采样的内容，还可以如何采样模拟信号，利用其在寻找光谱孔中的预期成功。该项目将探讨整体体系结构的复杂性，并最终评估认知MAB-FRI接收器的潜在好处。通过将学习算法移动到更近的一步，直接管理与物理世界的数据收购接口，这是对各种相关感应问题的广泛含义。该项目还将包括吸引学生参与教室的活动，展示本研究中使用的基本数学工具以及少数群体在研究项目中的少数群体，这些工具有助于推动自适应系统的广泛领域。