In this talk we examine the capacity of Markov channels
with feedback. One of the main difficulties in this problem has
to do with the fact that the transmitter and the receiver may have
different information about the state of the channel. We show how
to choose appropriate sufficient statistics at the transmitter and
receiver. We then formulate the capacity optimization problem as a
Markov decision problem (MDP). The resulting Bellman equation can
be viewed as a single-letter characterization of the capacity.
Bio: Sekhar Tatikonda is presently an associate professor of
electrical engineering at Yale University. He received his PhD
degree in EECS from MIT in 2000. He was a postdoctoral fellow
in EECS at UC- Berkeley from 2000-2002. His research interests
span topics in information theory, statistical AI, and stochastic
control. He received the NSF CAREER award in 2006.