Menu: Information For
- Prospective Students
- Corporate Partners
|go to week of Oct 28, 2012||28||29||30||31||1||2||3|
|go to week of Nov 4, 2012||4||5||6||7||8||9||10|
|go to week of Nov 11, 2012||11||12||13||14||15||16||17|
|go to week of Nov 18, 2012||18||19||20||21||22||23||24|
|go to week of Nov 25, 2012||25||26||27||28||29||30||1|
Both the number of devices capable of transmitting and receiving information in a wireless manner as well as the number of applications seeking this form of information exchange has exploded over recent times. Heterogeneity exists at all scales from network topology (cellular, pico and femto cells, for example) to the types of devices and applications. To achieve desired Quality-of-Service for all of these systems necessitates the design, optimization and control of networks of unprecedented size and complexity. In this talk, I will suggest the use of signal processing techniques typically applied to image processing-based applications to the design and control of wireless networks. In particular, we consider Markov Chain modeling for all network subsystems such as protocols, interference and channel conditions, as had been often employed in wireless network analysis. Network performance is measured via the value function which can be network throughput or revenue generated. We design graph wavelets for the purposes of employ linear approximations of the value function and examine sparse approximation methods employed with these wavelets. The proposed technique allows a considerable reduction in the number of observations needed for accurate estimation and further admits a low complexity method by which to design policies for the large-scale network. Numerical results show that anywhere from an order of magnitude less to half of the observations are needed by the new scheme relative to traditional learning schemes to estimate the value function. As our methods exploit graph structure and the finite-state machine nature of the model alone, we also discuss some other possible applications in biology and sensor networks.
Urbashi Mitra received the B.S. and the M.S. degrees from the University of California at Berkeley and her Ph.D. from Princeton University. After a six year stint at the Ohio State University, she joined the Department of Electrical Engineering at the University of Southern California, Los Angeles, where she is currently a Professor. She is a member of the IEEE Information Theory Society's Board of Governors (2002-2007, 2012-2014) and the IEEE Signal Processing Society’s Technical Committee on Signal Processing for Communications and Networks (2012-2014). She is the recipient of: 2012 Globecom Signal Processing for Communications Symposium Best Paper Award, 2012 NAE Lillian Gilbreth Lectureship, USC Center for Excellence in Research Fellowship (2010-2013), the USC Viterbi School of Engineering Dean’s Faculty Service Award (2009), USC Mellon Mentoring Award (2008), IEEE Fellow (2007), Texas Instruments Visiting Professor (Fall 2002, Rice University), 2001 Okawa Foundation Award, 2000 OSU College of Engineering Lumley Award for Research, 1997 OSU College of Engineering MacQuigg Award for Teaching, and a 1996 National Science Foundation (NSF) CAREER Award. Dr. Mitra has been/is an Associate Editor for the following IEEE publications: Transactions on Signal Processing (2012--), Transactions on Information Theory (2007-2011), Journal of Oceanic Engineering (2006-2011), and Transactions on Communications (1996-2001). Dr. Mitra has held visiting appointments at: the Delft University of Technology, Stanford University, Rice University, and the Eurecom Institute. She served as co-Director of the Communication Sciences Institute at the University of Southern California from 2004-2007. Her research interests are in: wireless communications, communication and sensor networks, detection and estimation and the interface of communication, sensing and control.