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: 1456793
• 负责人: Harold Vincent Poor
• 金额: $ 13.5万
• 依托单位: Princeton University
• 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: 2015-03-15 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1456793&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 underutilization 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.

扩展未来无线服务的一个关键任务是克服频谱紧缩。目前，静态分配和严格监管导致可用频谱资源利用不足。灵活的频谱使用旨在开发未充分利用的频谱。由于无线传输的高度动态特性，可用频谱机会可能是不连续的、分散在大带宽上，并且在有限的时间内在本地可用。这就迫切需要了解如何有效地、以最小的延迟发现、评估和利用时频位置变化的频谱资源。此外，以敏捷的方式访问已识别的空闲频谱至关重要。该项目将设计顺序推理和学习算法，以便在频谱状态快速变化时实现敏捷频谱访问。与逐块算法相比，顺序算法的主要优点是它们通常会显着减少决策延迟。该项目的总体目标是设计顺序推理和学习算法以实现敏捷的频谱利用。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关联但日益复杂和要求越来越高的任务：1）采用顺序强化学习和顺序推理算法来设计快速发现频谱机会的感知策略； 2）设计顺序算法以进行快速、准确的频谱质量评估； 3) 构建、维护和利用我们网络运行区域的干扰图，并将其表示为空间势场。拟议的研究预计将对应用和理论做出重大贡献。在应用层面，所提出的研究有可能通过引入序贯分析、机器学习和统计推断的新工具来设计频谱发现、评估和开发政策，从而大幅提高频谱效率。在理论层面上，所提出的项目将推进顺序分析的最先进水平，并为最佳停止、控制和机器学习问题的通用方法论基础贡献新方法。此外，还将开发使用空间势场、序列统计和高级传播建模来建模和利用干扰知识的新方法和理论。