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Events for October 08, 2010
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2010 NAE Grand Challenges National SummitFri, Oct 08, 2010
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Various, Various
Talk Title: 2010 NAE Grand Challenges National Summit
Host: USC Viterbi School of Engineering
More Info: http://www.naegrandchallengessummit2010.org/Location: George Finley Bovard Administration Building (ADM) -
Audiences: Everyone Is Invited
Contact: Leslie DaCruz
Event Link: http://www.naegrandchallengessummit2010.org/
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EE-Systems Seminar
Fri, Oct 08, 2010 @ 11:00 AM - 01:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Anima Anandkumar, U.C.Irvine
Talk Title: "Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret"
Abstract: I will talk about the problem of distributed learning and channel access in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users and sensing and access decisions are undertaken by them in a completely distributed manner. The challenge is to ensure that learning of channel availabilities and distributed channel access among the secondary users do not sacrifice the cognitive system throughput (number of successful secondary transmissions) to a large extent and to design policies which minimize this loss. We propose policies for distributed learning and channel access which achieve order-optimal cognitive system throughput under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the sum regret in distributed learning and access, which is the loss in secondary throughput due to learning and distributed access.
For the scenario when the number of secondary users is known to the policy, we prove that the total regret is logarithmic in the number of transmission slots. This policy achieves order-optimal regret based on a logarithmic lower bound for regret under any uniformly-good learning and access policy.
We then consider the case when the number of secondary users is fixed but unknown, and is estimated at each user through feedback. We propose a policy whose sum regret grows only slightly faster than logarithmic in the number of transmission slots. I will also talk about some exciting open problems in this context.
Biography: Anima Anandkumar received her B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Madras in
2004 and her MS and PhD degrees in Electrical Engineering from Cornell University, Ithaca, NY in 2009. She was at the Stochastic Systems Group at MIT, Cambridge, MA as a post-doctoral researcher. She has been an assistant professor at EECS Dept. and a member of center for pervasive communications and computing (CPCC) at U.C.Irvine since July 2010. She is the recipient of the 2009 Best Thesis Award by the ACM Sigmetrics Society, 2008 IEEE Signal Processing Society Young Author Best Paper Award, 2008 IBM Fran Allen PhD fellowship, and student paper award at 2006 IEEE ICASSP. Her research interests are in the area of statistical-signal processing, network theory and information theory.
Host: Bhaskar Krishnamachari
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Shane Goodoff
