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题 目：Big Data of a Large-Scale Cognitive Radio Network: Testbed, Data Representation and Analytics
报告人：Robert Caiming Qiu， Professor， Department of Electrical and Computer Engineering，Tennessee Technological University
时间：2:00pm，May 08, 2015
报告人简介：邱才明教授，中国千人计划国家特聘专家，美国田纳西理工大学电子与计算机工程系。Dr. Qiu has nearly 20 years of teaching and research in academia, industry and startup with diverse research experience in wireless communications and networks, wireless (and remote) sensing, Big Data and Smart Grid. His professional experiences outside the academia include GTE Labs (now Verizon Wireless), Bell Labs (Lucent Technologies) and the startup. He served as founder, CEO and President for Wiscom Technologies Inc. that grew to a total of 30+ staff and whose assets were sold to Intel. He is a US citizen (since 2001). He joined Tennessee Technological University (TTU) in 2003. In 2008, he was tenured and promoted to a full professor at TTU. He was also the principal investigator for a Congressional Earmark Project that has the planned budget of $ 5 millions in three phases, although only the first phase was actually funded due to the change of policy in US Congress. He served as the founding coordinator for two college-level focus areas: (1) Smart Grid and (2) Big Data. Dr. Qiu has attracted a total funding over $ 3 millions from diverse sources, including NSF, ARO, ONR, AFOSR and AFRL. He spent four summers in federal defense laboratories (AFRL and NRL).
内容提要： Network traffic monitoring and analysis is of theoretical and practical significance for optimizing network resource and improving user experience. We are interested in system operations for finite time horizon (say in the levels of several milliseconds). Previous work often deals with network-level traffic data such as packets and flow records. To our best knowledge, for the first time we capture, process and store, in a real-time manner, massive modulation waveforms (physical-layer level). The system has been deployed on-campus for a network of 100 nodes consisting of USRP soft-defined radios. First, we report some initial measurements from this testbed; we don’t pretend that we understand all the results. Second, we report our findings about how to represent the massive waveform datasets, with the help of large random matrices; we briefly introduce three relevant frameworks of random matrix theory: asymptotic, non-asymptotic and free probability. Third, we present some fundamental challenges and potential applications.