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

扩展未来无线服务的一个关键任务是克服频谱紧缩。目前，静态分配和刚性监管导致可用频谱资源未得到充分利用。灵活的频谱使用旨在利用未得到充分利用的频谱。可用的频谱机会可能是非连续的，分散在大带宽上，并且由于无线传输的高度动态性质，在本地和有限的时间段内可用。这就需要了解如何在最小延迟的情况下高效地发现、评估和利用时频位置变化的频谱资源。此外，灵活地接入识别出的空闲频谱是至关重要的，本项目将针对频谱状态快速变化的情况设计用于灵活频谱接入的顺序推理和学习算法。与分块算法相比，顺序算法的关键优势在于它们通常会显著减少决策延迟。这个项目的总体目标是设计顺序推理和学习算法，以实现灵活的频谱利用。特别是，该项目将使用先进的顺序推理和学习方法来完成以下三项相互关联但日益复杂和苛刻的任务：1)使用顺序强化学习和顺序推理算法来设计感知策略，以快速发现频谱机会；2)设计顺序算法以快速准确地评估频谱质量；以及3)建立、维护和利用我们的网络运营区域的干扰图，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重大贡献。在应用层面上，拟议的研究有可能通过引入序列分析、机器学习和统计推理的新工具来设计频谱发现、评估和开发策略，从而大幅提高频谱效率。在理论层面上，拟议的项目将推进顺序分析的最新水平，并为最优停车、控制和机器学习问题的一般方法学基础提供新的途径。此外，还将开发利用空间势场、顺序统计和高级传播建模来建模和利用干扰知识的新方法和新理论。

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.