Network management and configuration is an essential attribute of any wireless network with reliable self-tuning capabilities. However,the cost and overhead of network management has rarely been accounted for from a fundamental limit (information theoretic) perspective. In contrast to the past generations of networking solutions, on the other hand, in the ever-increasingly mobile and large-scale networks of tomorrow the network reconfiguration overhead may not be insignificant; this includes the initial beam alignment, link maintenance, spectrum sensing, packet resizing, etc. Our work aims to provide fundamental limits on the overhead associated with learning, network tuning, and optimization of network parameters.
Our approach relies on fundamental notions in information theory and statistics to quantify the networking overhead and utilizes recent data analytic and machine learning algorithms to develop practical learning/optimization algorithms. In the first part of the talk, we consider the problem of reliably and quickly searching for a parameter of interest in a large signal space in face of measurement-dependent noise. This problem naturally arises in many practical communications systems such as the directional link establishment and maintenance (beam alignment) as well as spectrum sensing for cognitive radios. In the second part of the talk, we consider an important variant of the search problem: data-driven (Bayesian and non-Bayesian) function maximization and its connection to network parameter tuning.
Tara Javidi studied electrical engineering at Sharif University of Technology, Tehran, Iran from 1992 to 1996. She received her MS degrees in electrical engineering (systems) and in applied mathematics (stochastic analysis) from the University of Michigan, Ann Arbor, in 1998 and 1999, respectively. She received her Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, in 2002.
From 2002 to 2004, Tara Javidi was an assistant professor at the Electrical Engineering Department, University of Washington, Seattle. In 2005, she joined the University of California, San Diego, where she is currently a professor of electrical and computer engineering. In 2013-2014, she spent her sabbatical at Stanford University as a visiting faculty. At the University of California, San Diego, Tara Javidi is a founding co-director of the Center for Machine-aware Computing and Security, directs the Advanced Networking Science Lab and is a faculty member of the Centers of Information Theory and Applications (ITA), Wireless Communications (CWC), and Networked Systems (CNS). She is also a member of Board of Governors of the IEEE Information Theory Society (2017/18/19).
Tara Javidi’s research interests are in theory of active learning, information theory with feedback, stochastic control theory, and stochastic resource allocation in wireless communications and communication networks. She was the guest editor for the IEEE Journal of Selected Areas in Communications special issue on Communications and Control. From 2011 to 2014, she was an associate editor for ACM/IEEE Transactions on Networking and the editor for the IEEE Information Theory Society Newsletter. She currently serves as an associate editor for IEEE Transactions on Information Theory and IEEE Transactions on Network Science and Engineering. Furthermore, she is currently on the board of the series "Foundations and Trends in Communications and Information Theory."