CIF:Small: A Tensor-based Framework for Reliable Radio Cartography

该项目的目标是设计一个有效的框架，通过生成可靠和动态的无线电环境地图(REMS)来实现射频(RF)频谱感知。这是朝着现实认知无线电网络(CRN)扩散迈出的显著一步，CRN能够实现频谱共享，以缓解频谱稀缺的问题。这种频谱的高效利用赋予了不断增加的应用和服务以巨大的影响，对国民健康、福利、公共安全和经济增长产生了巨大影响。此外，开发的数据分析工具为高维信号采样和处理提供了独特的解决方案，使其能够在大数据、物联网和无线传感器网络等广泛应用中进一步发展，从而促进社会和经济进步。该项目通过新课程的开发和修订，将研究和教育整合在一起，并让代表性不足的少数族裔、研究生和本科生参与研究。该研究弥合了张量数据分析理论研究与无线通信和网络之间的差距，以促进频谱感知。研究人员将开发一个基于张量的框架，以生成动态和可靠的REMS，其中包括RF信号在空间、时间和频率上的功率分布。基于张量的分析促进了CRN中存在的固有属性和数据结构以及主网络行为的先验知识的集成。研究了贝叶斯张量分解和基于结构的张量分解，研究了张量分解的存在唯一性条件。分解的结果被用来获得传感器读数的基于模型的内插并产生REM。对应于REM，将创建可靠性图，并将其用于最佳联合频谱传感器和通道选择。

The goal of this project is to devise an efficient framework for achieving radio frequency (RF) spectrum awareness through generating reliable and dynamic radio environment maps (REMs). This is a notable step towards the proliferation of realistic cognitive radio networks (CRNs), which enable spectrum sharing to alleviate the problem of spectrum scarcity. This efficient use of spectrum empowers ever-increasing applications and services with a great impact on national health, welfare, public safety, and economic growth. In addition, the developed data analysis tools provide a distinctive solution to high-dimensional signal sampling and processing, enabling further development in a wide range of applications, such as big data, Internet of Things, and wireless sensor networks, which can promote social and economic progress. This project integrates research and education through new course development and revisions, and involving underrepresented minorities, graduate, and undergraduate students in research.This research bridges the gap between the theoretical research in tensor data analysis and wireless communications and networking to facilitate spectrum awareness. The investigators will develop a tensor-based framework to generate dynamic and reliable REMs that include RF signal power distribution over space, time and frequency. Tensor-based analysis facilitates the integration of the inherent properties and data structures that exist in CRNs as well as the prior knowledge of the behavior of the primary network. Bayesian and structure-based tensor decomposition is investigated, for which existence and uniqueness conditions are studied. The outcome of the decomposition is employed to obtain a model-based interpolation of sensor readings and to generate the REM. Corresponding to an REM, a reliability map will be created and utilized for optimal joint spectrum sensor and channel selection.