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1200 East California Blvd M/C 136-93 116-B Moore Pasadena, California 91125 Phone: (626) 395 - 2219 soofsoof@caltech.edu |
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Last update: 4/6/09.
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Short Bio; Research Interests; Current Research; Publications; Dissertations; Recent talks/posters; Pictures; |
| Short Bio:
I
am currently a postdoc at Caltech,
working with Prof.
Michelle Effros on problems in network information theory. The projects are part of the ITMANET-FLoWS (Fundamental Limits of Wireless Systems) research. I received my B.Sc., M.Sc., and Ph.D. in electrical
engineering
from the Technion
- I.I.T., supervised by Prof. Neri Merhav and Prof. Tsachy Weissman. |
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Abstract: Consider the problem of lossless source coding for networks with side
information. This model captures scenarios where the network capacity
is insufficient to describe the source to its intended destinations,
yet, it can still be delivered without loss provided there is
sufficient capacity from a helper. The problem of source coding with
side information has numerous applications, from sensor networks, where
transmission occurs through nodes which may have correlated data, to
multimedia networks, where intermediate nodes may have, for example, a
lower-resolution version of the required information.
While several spacial cases of this problem have been addressed in the current literature (e.g., the three-node network of Ahlswede and Korner), the general problem remains unsolved. In this work, we derive inner and outer bounds on the rate region and describe sufficient conditions for the tightness of these bounds. Our approach demonstrates how strategies intended for small canonical problems, combined with network coding, can tackle complex networks, while still inheriting the desirable properties of the building blocks used. Furthermore, due to the complexity of solving large networks, it is highly desirable to identify the key parameters which dictate their rate region. This work substantially extends the network scenarios for which maxflow-mincut analysis is know to describe the rate region in full. Finally, in this work we open a new connection between networking and successive refinement of information.
Abstract: In this work, we
address the following question: “When can we guarantee the
optimality of linear coding at all internal nodes of a network?”
While sufficient conditions for linear coding throughout the network
are known, it is not clear whether relaxing the linearity constraints
at the terminal nodes can result in simpler operations at the internal
nodes. We present a novel method to analyze the space of network
solutions using the constraints resulting from the network topology,
and we identify sufficient conditions for an optimal linear solution at
all internal nodes. These conditions are also sufficient to
characterize the rate region only in terms of Shannon information
inequalities and to describe the rate region as a cone hull of random
vectors over a finite alphabet, independently of any block length.
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Some recent photos can be found here and here.
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