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

扩展未来无线服务的关键是克服频谱紧缩。目前，静态分配和刚性监管导致可用频谱资源利用率不足。灵活的频谱使用旨在利用未充分利用的频谱。由于无线传输的高度动态性质，可用频谱机会可以是不连续的、分散在大带宽上的，并且在本地可用并且在有限的时间段内可用。这激发了理解如何有效地并且以最小延迟发现、评估和利用时频位置变化频谱资源的需要。此外，以敏捷的方式访问已识别的空闲频谱是至关重要的，本项目将设计顺序推理和学习算法，当频谱的状态快速变化的敏捷频谱访问。与块式算法相比，顺序算法的主要优点是它们通常会显著减少决策延迟。这个项目的首要目标是设计顺序推理和学习算法，灵活的频谱利用率。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苛刻的任务：1）采用顺序强化学习和顺序推理算法来设计用于快速频谱机会发现的感知策略; 2）设计用于快速和准确的频谱质量评估的顺序算法;以及3）建立、维护和利用我们的网络运行的区域的干扰图，并将其表示为空间势场。预计该研究将在应用和理论上做出实质性贡献。在应用层面上，拟议的研究有可能大大提高频谱效率，通过引入新的工具，从顺序分析，机器学习和统计推断的频谱发现，评估和开发政策的设计。在理论层面上，拟议的项目将推进序列分析的最新技术，并为最佳停止，控制和机器学习问题的一般方法基础提供新的方法。此外，将开发新的方法和理论，利用空间势场，顺序统计和先进的传播建模的干扰建模和利用知识。

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.