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（称为有限创新率（FRI）采样）以及最先进的学习技术来在线探索无线电频谱。我们计划使用完善的多臂老虎机（MAB）问题框架，该框架对认知无线电代理的情况进行建模，该代理同时尝试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该框架与这种新颖的模数接收器前端相结合，采样率低于所谓的奈奎斯特极限，自适应调整采样结构中的参数，以感知比之前考虑的更广泛频率范围内的频谱机会可能的，特别是低于在不适应的情况下近视所能达到的水平。 我们的研究成果是一个认知传感器的内聚系统模型，它配备了一个决策引擎，不仅可以优化采样内容，还可以优化如何采样模拟信号，利用其在寻找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 MAB-FRI 接收器的潜在优势。通过将学习算法更进一步地直接管理物理世界的数据采集接口，该研究对各种相关的传感问题产生了广泛的影响。该项目还将包括让学生在课堂上展示本研究中使用的基本数学工具的活动，以及让少数群体参与研究项目的活动，这些项目有助于推进自适应系统的广泛领域。