NeTS: Small:Spatio-Temporal Spectrum Sensing

该项目旨在开发一种综合的方法来描述射频环境的空间和时间特征，包括：i)活动发射机的数量，ii)它们的功率、到达方向和位置，以及iii)调制类别。时空频谱感知需要新颖的多维参数估计算法，而不是传统的基于假设检验的检测算法。在这项工作中，利用到达角测量的贝叶斯估计方法，在考虑测量噪声和不确定性的情况下，在给定区域内建立发射机存在的概率图。应用随机矩阵理论中的工具进行联合检测和参数估计，并解析地推导出性能界。调制分类的方法基于降低了采样复杂度的拟合优度统计检验，并为大范围的调制类别提供了统一的分类框架。这项研究的结果将影响新型媒体接入和路由协议的设计，这些协议通过了解该地区其他发射机的位置和链路质量来管理干扰。开发的技术可能会应用于无线基础设施中的射频传输监控，以及国防和国家安全应用。

This project aims to develop a comprehensive approach for characterization of RF environment in terms of spatial and temporal features including: i) the number of active transmitters, ii) their power, direction of arrival and location, and iii) modulation class. Spatio-temporal spectrum sensing requires novel multi-dimensional parameter estimation algorithms as opposed to conventional spectrum sensing that uses hypothesis testing based detection algorithms. In this work, the Bayesian estimation approach using angle-of-arrival measurements is applied to create the probabilistic map of the transmitter presence in a given region taking into account measurement noise and uncertainties. The tools from random matrix theory are applied to perform joint detection and parameter estimation, and analytically derive performance bounds. The methods for modulation classification are based on goodness-of-fit statistical tests with reduced sampling complexity, and provide the unified classification framework for a wide range of modulation classes. The results of this research will impact the design of novel medium access and routing protocols that manage the interference through awareness of the location and link quality of other transmitters in the region. The developed technologies could potentially apply for monitoring of RF transmissions within wireless infrastructure, and for the defense and national security applications.