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，使认知无线电接收器能够使用最先进的模数转换形式，即有限创新率(FRI)采样与最先进的学习技术相结合，在线探索无线电频谱。我们计划使用多臂强盗(MAB)问题的成熟框架，该框架对认知无线电代理的情况进行建模，该代理同时试图获取新知识并根据先前学到的知识优化其决策。我们的主要贡献在于将这一框架与这一新颖的模数转换接收器前端相结合，使采样率低于所谓的奈奎斯特极限，自适应地调整采样结构中的参数，以在比以前认为可能的频率范围更广的范围内感知频谱机会，特别是在没有适配的情况下，进一步低于近视所能达到的范围。我们研究的结果是一个认知传感器的内聚系统模型，它被赋予了一个决策引擎，不仅可以优化采样什么，而且可以优化如何采样模拟信号，利用它在发现频谱空洞方面的预期成功。该项目将探索整体架构的复杂性，并最终评估认知型MAB-FRI接收器的潜在好处。通过将学习算法向直接管理物理世界的数据采集接口更近一步，这项研究将广泛应用于各种相关的传感问题。该项目还将包括让学生在课堂上介绍这项研究中使用的基本数学工具的活动，以及让少数群体参与有助于推动适应系统广泛领域的研究项目的活动。