EAGER: Spectrum Situational Awareness-Understanding the Data

EAGER：频谱态势感知 - 理解数据

• 批准号: 1454835
• 负责人: Magdy Bayoumi
• 金额: $ 18.08万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454835&HistoricalAwards=false
• 关键词: EAGER; Spectrum; Situational; Awareness; Understanding

The key resource shared by all wireless systems is the electromagnetic spectrum, which is challenged by temporal and spatial congestion, interference, and increasing security threats. This project investigates the hypothesis that building a resilient always-on wireless network, capable of meeting ever-increasing performance and security requirements, necessitates not only innovations in radio design but also innovations in the management and exploitation of vast amounts of radio spectrum knowledge. Such knowledge is referred to as spectrum situational awareness - the amount of actionable intelligence and understanding that a wireless network (or individual radio) has about its RF spectrum environment. This research work formally studies the nature of big spectrum data and investigates several critical issues, e.g., how should data from heterogeneous sources be prioritized or weighted, and what are the effects of inaccurate or sparse data measurements.The scientific goals of this project are to provide foundational principles and insights for developing innovative and scalable spectrum situational awareness architectures, and to enable more efficient and effective uses of the data as well as the improved protocol and system designs. Using formal statistical methods (e.g., regression analysis and response surface methodology) and new big data analytics tools, the research team fully characterizes big spectrum data, its potential impact on spectrum utilization and system performance, and its interaction with the plethora of influential radio, network, and environmental factors. The empirical work is based on experimental scenarios that reflect realistic networking environments, comprising multiple wireless access technologies, interconnected high-speed backhaul networks, and distributed cloud-based technologies. Two testbed instruments are used to design and execute large-scale simulation and emulation experiments: the Distributed Computing and Visual Analytics Sandbox for High Volume Data Streams combined with the Wireless Systems and Performance Engineering Research (WiSPER) laboratory. Additionally, the research team develops a multilayer spectrum situational awareness framework and is deriving performance indices that can be used to quickly quantify and compare the spectrum situational awareness capabilities of network systems. The project engages students from underrepresented groups via ongoing partnerships with Historically Black Colleges and Universities.

所有无线系统共享的关键资源是电磁频谱，它受到时间和空间拥塞，干扰和日益增加的安全威胁的挑战。该项目研究的假设是，建立一个有弹性的始终在线的无线网络，能够满足不断增长的性能和安全要求，不仅需要在无线电设计的创新，而且还需要在管理和利用大量的无线电频谱知识的创新。这种知识被称为频谱态势感知-无线网络（或单个无线电）对其RF频谱环境的可操作情报和理解的量。这项研究工作正式研究了大频谱数据的性质，并调查了几个关键问题，例如，该项目的科学目标是为开发创新和可扩展的频谱态势感知架构提供基本原则和见解，并使数据的使用更加高效和有效，以及改进的协议和系统设计。使用正式的统计方法（例如，回归分析和响应面方法）和新的大数据分析工具，研究团队充分描述了大频谱数据的特征，其对频谱利用率和系统性能的潜在影响，以及其与大量有影响力的无线电，网络和环境因素的相互作用。实证工作是基于反映现实网络环境的实验场景，包括多种无线接入技术，互联高速回程网络和分布式云技术。两个试验台仪器用于设计和执行大规模的模拟和仿真实验：分布式计算和可视化分析沙箱的大容量数据流结合无线系统和性能工程研究（WiSPER）实验室。此外，研究小组还开发了一个多层频谱态势感知框架，并正在推导性能指标，这些指标可用于快速量化和比较网络系统的频谱态势感知能力。该项目通过与历史上黑人学院和大学的持续合作关系，吸引了来自代表性不足群体的学生。

项目成果

An Energy-Detection-Based Cooperative Spectrum Sensing Scheme for Minimizing the Effects of NPEE and RSPF

用于最小化 NPEE 和 RSPF 影响的基于能量检测的协作频谱感知方案

• DOI: 10.1145/2988287.2989169
• 发表时间: 2016
• 期刊: Analysis and Simulation of Wireless and Mobile Systems - MSWiM '16
• 作者: Olaleye, Oladiran G.;Iqbal, Muhammad;Aly, Ahmed;Perkins, Dmitri;Bayoumi, Magdy
• 通讯作者: Bayoumi, Magdy