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: 1318748
• 负责人: Yingying Chen
• 金额: $ 30.48万
• 依托单位: Stevens Institute of Technology
• 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 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318748&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。准确和鲁棒的频谱感测和未授权频谱使用的识别是未来无线电系统中频谱效率的重要组成部分。该项目旨在利用基于共识的协作，具有自组织和可扩展的网络结构，以捕获频谱用户的群集行为，并提供合作频谱感知在一个完全分布式的方式。该项目将控制理论和机器学习技术相结合，设计了一种安全的加权平均一致性协作频谱感知算法，该算法不仅能够捕获异构设备的CRN中的群集行为，而且对实际信道条件具有鲁棒性。基于动态信号强度映射的鲁棒定位方法具有定位多个恶意用户的能力。此外，新技术使用实际测试平台进行验证，并向工业合作者进行现场部署和系统演示。控制理论与动态频谱接入技术的结合将为CRN中提高频谱利用率带来新的革命。该项目是利用多学科知识的先驱（例如，控制系统和机器学习技术）以在未来无线电系统中实现更有效的频谱使用，旨在缓解频谱占用的日益拥挤并支持异构设备的共存。该项目还开展广泛的教育和外联活动，鼓励学生从事科学和工程领域的职业。研究结果将通过在会议和期刊上的介绍和出版物传播给学术界和工业界。