Collaborative Research: Design, Analysis and Implementation of Social Interactions in Cognitive Radio Networks

协作研究：认知无线电网络中社交互动的设计、分析和实现

• 批准号: 1247778
• 负责人: Robert Qiu
• 金额: $ 11万
• 依托单位: Tennessee Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247778&HistoricalAwards=false
• 关键词: Collaborative; Research; Design; Analysis; Implementation

Cognitive radio is an efficient approach to access frequency spectrum. The performance of cognitive radio networks is substantially determined by the information on spectrum occupancies. Experiments have demonstrated the temporal and spatial correlations of spectrum availability, which is of key importance in the design and analysis of cognitive radio networks. Motivated by the observation, this research studies the social interaction mechanism for secondary users to fully exploit the correlations. A recommendation mechanism is useful for secondary users to share correlated information about the spectrum. Collaborative filtering can enhance the capability of better learning the spectrum situations. For better understand and assist the design, analysis is conducted for the social interaction mechanism, based on powerful tools in social networks, such as master equation, mean filed dynamics and epidemic propagation. Continuum model, such as partial differential equations for diffusions, is also employed as the limit case of discrete cognitive radio networks. Beacon based and packet based mechanisms are used for the recommendation protocols. A 100-node hardware cognitive radio network testbed is built to demonstrate the proposed mechanisms, algorithms and protocols. The research involves aspects of wireless communications, networking, artificial intelligence and physics; thus the inter-disciplinary essence of the research also lends itself to cross-disciplinary education. New courses are devised, which involve the topics of cognitive radio networks, machine learning and image processing. This project also attracts traditionally underrepresented groups, as well as outreach high school students.

认知无线电是一种有效的频谱接入方式。认知无线电网络的性能在很大程度上取决于频谱占用信息。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，本研究探讨二次使用者的社会互动机制，以充分利用相关性。推荐机制对于次用户共享关于频谱的相关信息是有用的。协同滤波可以增强对频谱情况的学习能力。为了更好地理解和辅助设计，基于社交网络中的有力工具，如主方程、均场动力学和传染病传播，对社会交互机制进行了分析。连续介质模型，如扩散偏微分方程，也被用作离散认知无线电网络的极限情况。基于信标和基于分组的机制用于推荐协议。建立了一个100节点的硬件认知无线电网络实验平台，对所提出的机制、算法和协议进行了验证。该研究涉及无线通信，网络，人工智能和物理学等方面;因此，该研究的跨学科本质也适用于跨学科教育。设计了新的课程，涉及认知无线电网络，机器学习和图像处理的主题。该项目还吸引了传统上代表性不足的群体以及外展高中学生。