TOPIC Delay and the gap between the worst and average cases in communication 
AREA Communications & Networks  
SPEAKER Prof. Anand Sarwarte, Rutgers University 
DATE 21 December 2016, Wednesday 
TIME 4pm to 5pm 
VENUE E4-04-06 , Engineering Block E4, Faculty of Engineering, NUS  
FEES No Charge 

The information theory community has traditionally studied two different models for communication. The Shannon-theoretic model treats the channelís impact as random, so codes must correct almost all error patterns of a given weight; this as an average-case analysis. The coding-theoretic (Hamming-theoretic?) model treats the channel as adversarial, so codes must correct all error patterns of a given weight; this is a worst-case analysis. Between the two lie several different channel models which can be usefully described in the language of arbitrarily varying channels (AVCs). In an AVC, the communication channel has two inputs at each time, one for the encoder and one for a state that is controlled by an adversary who wishes to foil the communication. The difference between average- and worst-case can be captured by changing the information available to the adversary. In this talk I will describe this model and recent results on how the adversary can and cannot benefit from partial knowledge of the transmitted codeword. In particular, I will discuss how knowledge of delayed or future encoder inputs affect the channel capacity in sometimes surprising ways. 

Anand D. Sarwate is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers, the State University of New Jersey. He received B.S. degrees in Electrical Engineering and Mathematics from MIT in 2002 and a PhD in Electrical Engineering from UC Berkeley in 2008. He is the Online Editor of the IEEE Information Theory Society (2015-) and an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks (2015-). Prof. Sarwate received the CAREER award from the US National Science Foundation in 2015. His interests are in information theory, machine learning, and signal processing, with applications to distributed systems, privacy and security, and biomedical research.

Host: Asst Prof Vincent Tan 

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