NeTS: Small: Collaborative Research: Distributed Robust Spectrum Sensing and Sharing in Cognitive Radio Networks

NetS：小型：协作研究：认知无线电网络中的分布式鲁棒频谱感知和共享

• 批准号: 1318751
• 负责人: Jie Yang
• 金额: $ 15.01万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318751&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Distributed

The future Cognitive Radio Networks (CRNs) will consist of heterogeneous devices such as smartphones, tablets and laptops moving dynamically. Accurate and robust spectrum sensing and identification of unauthorized spectrum usage are essential components of spectral efficiency in future radio systems. This project aims to utilize consensus-based cooperation featuring self-organizable and scalable network structure to capture the swarming behaviors of spectrum users and providing cooperative spectrum sensing in a fully distributed manner. By using a combination of control theory and machine learning techniques, the project designs secure weighted average consensus for cooperative spectrum sensing that can not only capture the swarming behaviors in CRNs with heterogeneous devices, but also is robust to practical channel conditions. Robust localization approaches are developed grounded on dynamic signal strength mapping, which have the capability to localize multiple malicious users. Additionally, the new techniques are validated using an actual testbed with on-campus deployment and system demonstration to industrial collaborators. The integration of control theory with dynamic spectrum access will enable a new revolution in the way for enhancing spectrum efficiency in CRNs. The project serves as a pioneer in exploiting multi-disciplinary knowledge (e.g., control systems and machine learning techniques) to achieve a more efficient spectrum usage in future radio systems, aiming to alleviate the increasing crowdness of the spectrum occupancy and support the co-existence of heterogeneous devices. This project also carries out a broad range of education and outreach activities to encourage students to pursue careers in the fields of science and engineering. Research results will be disseminated to academia and industry through presentations and publications in meetings, conferences and journals.

未来的认知无线电网络（CRN）将由动态移动的智能手机、平板电脑和笔记本电脑等异构设备组成。准确而强大的频谱感知和未经授权的频谱使用识别是未来无线电系统频谱效率的重要组成部分。该项目旨在利用具有自组织和可扩展网络结构的基于共识的合作来捕获频谱用户的集群行为，并以完全分布式的方式提供合作频谱感知。通过结合控制理论和机器学习技术，该项目设计了用于协作频谱感知的安全加权平均共识，不仅可以捕获具有异构设备的 CRN 中的集群行为，而且对于实际信道条件也具有鲁棒性。鲁棒的定位方法是基于动态信号强度映射而开发的，能够定位多个恶意用户。此外，新技术还使用实际测试台进行了验证，并向工业合作者进行了校园部署和系统演示。控制理论与动态频谱接入的结合将为提高CRN频谱效率的方式带来新的革命。该项目是利用多学科知识（例如控制系统和机器学习技术）在未来无线电系统中实现更有效频谱使用的先驱，旨在缓解频谱占用日益拥挤的情况并支持共存的异构设备。该项目还开展广泛的教育和推广活动，鼓励学生在科学和工程领域追求职业生涯。研究成果将通过会议、会议和期刊上的演示和出版物传播给学术界和工业界。