Prof. Ram Zamir
About Me
Ram Zamir has been consulting in the areas of radar and communications (DSL and WiFi), where he was involved with companies like Orckit and Actelis. During the period 2005-2014 he was the Chief Scientist of Celeno Communications. He has been teaching information theory, data compression, random processes, communications systems and communications circuits at Tel Aviv University.
He is an IEEE fellow since 2010. He served as an Associate Editor for Source Coding in the IEEE transactions on Information Theory (2001-2003), headed the Information Theory Chapter of the Israeli IEEE society (2000-2005), was a member of the BOG of the society
(2013-2015), and organized the information theory workshop ITW 2015 in Jerusalem. His research interests include information theory (in particular: lattice codes for multi-terminal problems), source coding, communications and statistical signal processing. His book "Lattice coding for signals and networks" was published in Cambridge University Press in 2014.
A Structured Coding Approach to Quantization, Modulation and Multiuser Information Theory.
** Cambridge University Press, August 2014**
See http://www.amazon.com/Lattice-Coding-Signals-Networks-Quantization/dp/0521766982#reader_0521766982.
Why to write a book (on lattice codes)? a seminar for the celebration of the publication, Oct. 20, 2014
Latest Publication
IEEE Transactions on Information Theory
Jan Østergaard, Uri Erez, Ram Zamir
It is well known that independent (separate) encoding of K correlated sources may incur some rate loss compared to joint encoding, even if the decoding is done jointly. This loss is particularly evident in the multiple descriptions problem, where it is the same source that is encoded in each description. We observe that under mild conditions about the source and distortion measure, the sum-rate of K separately encoded individually good descriptions tends to the rate-distortion function of the joint decoder in the limit of vanishing small coding rates of the descriptions. Moreover, we then propose to successively encode the source into K independent descriptions in each round in order to achieve a final distortion D after M rounds. We provide two examples – a Gaussian source with mean-squared error and an exponential source with one-sided error – for which the excess rate vanishes in the limit as the number of
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