NeTS: Small: Networking over Random Fields: A Statistical Model for Cognitive Radio Networks

实验证明了频谱可用性的时间和空间相关性，这是在认知无线电网络的设计和分析的关键。基于此，本研究应用描述多个相关随机变量行为的随机场理论，在时间和空间域上对频谱可用性进行建模。对于全球频谱活动，使用均匀随机场的伊辛模型来建模的空间相关性和分析的性能。对于局部频谱活动，贝叶斯网络用于描述频谱中的因果关系，并统计推断未来的频谱情况。在此基础上，利用受控随机场模型设计了认知无线电网络的组网协议。设计了一种低成本的光谱传感器，用于同时采集多个位置的真实的光谱测量值。该研究促进了对频谱活动的理解，并增强了下一代认知无线电网络的设计和分析。该研究涉及无线通信，网络，人工智能和图像处理方面;因此，该研究的跨学科本质也适用于跨学科教育。将设计新颖的课程，其中涉及认知无线电网络，机器学习和图像处理的主题。该项目还预计将吸引传统上代表性不足的群体，以及外展高中学生。

Experiments have demonstrated the temporal and spatial correlations of spectrum availability, which are of key importance in the design and analysis of cognitive radio networks. Motivated by the observation, this research applies the theory of random fields, which describes the behavior of multiple correlated random variables, to model the spectrum availabilities in time and space domains. For global spectrum activity, a homogeneous random field like Ising model is used to model the spatial correlation and analyze the performance. For local spectrum activities, Bayesian networks are used to describe the causality in spectrum and statistically infer the future spectrum situations. Furthermore, the model of controlled random fields is employed to design the networking protocols in cognitive radio networks. A low-cost spectrum sensor is designed to collect the real spectrum measurement in multiple locations simultaneously. The research promotes the understanding of frequency spectrum activities and enhances the design and analysis of the next generation cognitive radio networks. The research involves aspects of wireless communications, networking, artificial intelligence and imaging processing; thus the inter-disciplinary essence of the research also lends itself to cross-disciplinary education. Novel courses will be devised, which involve the topics of cognitive radio networks, machine learning and image processing. This project also expects to attract traditionally underrepresented groups, as well as outreach high school students.