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Workshop on Cognition and Control

Network coding - a rapid tutorial; Joint source, channel and network coding - a simple ad-hoc approach inspired from com
Muriel M├ędard
Muriel Médard is a Professor in the Electrical Engineering and Computer Science at MIT. She was previously an Assistant Professor in the Electrical and Computer Engineering Department and a member of the Coordinated Science Laboratory at the University of Illinois Urbana-Champaign. From 1995 to 1998, she was a Staff Member at MIT Lincoln Laboratory in the Optical Communications and the Advanced Networking Groups. Professor Médard received B.S. degrees in EECS and in Mathematics in 1989, a B.S. degree in Humanities in 1990, a M.S. degree in EE 1991, and a Sc D. degree in EE in 1995, all from the Massachusetts Institute of Technology (MIT), Cambridge. She has served as an Associate Editor for the Optical Communications and Networking Series of the IEEE Journal on Selected Areas in Communications, as an Associate Editor in Communications for the IEEE Transactions on Information Theory and as an Associate Editor for the OSA Journal of Optical Networking. She has served as a Guest Editor for the IEEE Journal of Lightwave Technology, the Joint special issue of the IEEE Transactions on Information Theory and the IEEE/ACM Transactions on Networking on Networking and Information Theory and the IEEE Transactions on Information Forensic and Security: Special Issue on Statistical Methods for Network Security and Forensics. She serves as an associate editor for the IEEE/OSA Journal of Lightwave Technology. She serves on the board of Governors of the IEEE Information Theory Society as well as serving as the first Vice President in 2011 and the President in 2012. She has served as TPC co-chair of ISIT, WiOpt and CONEXT. Professor Médard's research interests are in the areas of network coding and reliable communications, particularly for optical and wireless networks.
We overview the main aspects of network coding viewed as algebraic generalization of routing over networks. Using this approach, we show that distributed random linear network coding is with high probability optimal for multicast connections, even when the sources are correlated, thus generalizing Slepian-Wolf to networks. Moreover, such an approach readily handles erasures under fairly loose ergodicity constraints. Multicast connections using network coding lend themselves to very simple optimization, which in effect provide a relaxation of the integrality constraints of convex combinations of Steiner trees. We conclude by illustrating some practical uses of network coding, particularly in wireless networks.
February 21st, February 22nd
9:00 am, 3:00pm
Larsen 234

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