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

认知无线电是一种有效的频谱获取方法。认知无线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空相关性，这对认知无线电网络的设计和分析至关重要。在观察的激励下，本研究对二级用户的社会互动机制进行了研究，以充分利用相关性。推荐机制有助于辅助用户共享有关频谱的相关信息。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，我们利用社交网络中强大的工具，如主方程、平均场动力学、流行病传播等，对社交机制进行了分析。连续统模型，如扩散的偏微分方程，也被用作离散认知无线电网络的极限情况。推荐协议采用了基于信标和基于包的机制。建立了一个100节点硬件认知无线网络测试平台，对所提出的机制、算法和协议进行了验证。研究涉及无线通信、网络、人工智能和物理等方面；因此，研究的跨学科本质也适用于跨学科教育。新课程被设计出来，涉及认知无线电网络、机器学习和图像处理等主题。这个项目也吸引了传统上代表性不足的群体，以及外联的高中生。

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.