WiFiUS: Collaborative Research: Sequential Inference and Learning for Agile Spectrum Use

WiFiUS：协作研究：敏捷频谱使用的顺序推理和学习

• 批准号: 1660128
• 负责人: Lifeng Lai
• 金额: $ 8.6万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660128&HistoricalAwards=false
• 关键词: WiFiUS; Collaborative; Research; Sequential; Inference

A key imperative to expanding future wireless services is to overcome the spectral crunch. At present, static allocation and rigid regulation lead to under utilization of available spectral resources. Flexible spectrum use aims at exploiting under-utilized spectrum. Available spectrum opportunities may be non-contiguous, scattered over a large bandwidth, and are available locally and for a limited period of time due to the highly dynamic nature of wireless transmissions. This fuels the need to understand how to discover, assess and utilize the time-frequency-location varying spectral resources efficiently and with minimal delay. Moreover, it is critical to access identified idle spectrum in an agile manner.This project will design sequential inference and learning algorithms for agile spectrum access when the state of the spectrum varies rapidly. The key advantage of sequential algorithms, as compared to block-wise algorithms, is that they typically lead to significantly reduced decision delays. The overarching goal of this project is to design sequential inference and learning algorithms for agile spectrum utilization. In particular, this project will employ advanced sequential inference and learning methods for the following three interconnected yet increasingly sophisticated and demanding tasks: 1) to employ sequential reinforcement learning and sequential inference algorithms to design sensing policies for rapid spectrum opportunities discovery; 2) to design sequential algorithms for fast and accurate spectrum quality assessment; and 3) to build, maintain and exploit an interference map of the area where our network operates and represent it as a spatial potential field. The proposed research is expected to make substantial contributions to both applications and theory. On the application level, the proposed research has the potential to substantially improve spectral efficiency by introducing novel tools from sequential analysis, machine learning and statistical inference for the design of spectrum discovery, assessment and exploitation policies. On the theoretical level, the proposed project will advance the state of the art in sequential analysis and contribute new approaches to the general methodological base for optimal stopping, control and machine learning problems. Furthermore, new methods and theory of modeling and exploiting knowledge of interference using spatial potential fields, sequential statistics and advanced propagation modeling will be developed.

扩大未来无线服务的关键是要克服光谱紧缩。目前，静态分配和严格的调节导致可用光谱资源的利用。灵活的光谱使用旨在利用未充分利用的光谱。可用的频谱机会可能是无关紧要的，散布在大带宽上，并且由于无线传输的高度动态性质而在本地可用，并且在有限的时间内可用。这促使人们有必要了解如何有效地发现，评估和利用时频定位的光谱资源，并且以最小的延迟变化。此外，以敏捷的方式访问确定的闲置频谱至关重要。当频谱状态迅速变化时，该项目将设计顺序推理和学习算法的顺序推理和学习算法。与块算法相比，顺序算法的主要优点是它们通常导致决策延迟大大减少。该项目的总体目标是设计敏捷光谱利用率的顺序推理和学习算法。特别是，该项目将采用先进的顺序推理和学习方法来完成以下三个相互联系但越来越复杂且苛刻的任务：1）采用顺序增强学习和顺序推理算法来设计快速频谱机会发现的感应政策； 2）设计顺序算法，以进行快速准确的光谱质量评估； 3）要构建，维护和利用我们网络运行的区域的干扰图，并将其表示为空间电位字段。拟议的研究有望为应用和理论做出重大贡献。在应用程序级别上，拟议的研究有可能通过从顺序分析，机器学习和统计推断进行频谱发现，评估和开发政策的设计中引入新颖的工具，从而实质上提高光谱效率。在理论层面上，拟议的项目将在顺序分析中推进最新技术，并为最佳停止，控制和机器学习问题的一般方法基础贡献新方法。此外，将开发使用空间潜在领域，顺序统计和高级传播建模的新方法和建模和利用干扰知识的理论。