IRWIN AND JOAN JACOBS CENTER FOR COMMUNICATION AND INFORMATION TECHNOLOGIES Large Deviations Analysis of Variable-Rate Slepian-Wolf Coding

IRWIN AND JOAN JACOBS CENTER FOR COMMUNICATION AND INFORMATION TECHNOLOGIES Erasure/List Exponents for Slepian-Wolf Decoding

IRWIN AND JOAN JACOBS CENTER FOR COMMUNICATION AND INFORMATION TECHNOLOGIES Subset-sum phase transitions and data compression

We propose a rigorous analysis approach for the subset sum problem in the context of lossless data compression, where the phase transition of the subset sum problem is directly related to the passage between ambiguous and non–ambiguous decompression, for a compression scheme that is based on specifying the sequence composition. The proposed analysis lends itself to straightforward extensions in...

IRWIN AND JOAN JACOBS CENTER FOR COMMUNICATION AND INFORMATION TECHNOLOGIES Statistical Physics of Random Binning

We consider the model of random binning and finite–temperature decoding for Slepian–Wolf codes, from a statistical–mechanical perspective. While ordinary random channel coding is intimately related to the random energy model (REM) – a statistical–mechanical model of disordered magnetic materials, it turns out that random binning (for Slepian–Wolf coding) is analogous to another, related statist...

IRWIN AND JOAN JACOBS CENTER FOR COMMUNICATION AND INFORMATION TECHNOLOGIES Universal Decoding for Source–Channel Coding with Side Information

We consider a setting of Slepian–Wolf coding, where the random bin of the source vector undergoes channel coding, and then decoded at the receiver, based on additional side information, correlated to the source. For a given distribution of the randomly selected channel codewords, we propose a universal decoder that depends on the statistics of neither the correlated sources nor the channel, ass...

IRWIN AND JOAN JACOBS CENTER FOR COMMUNICATION AND INFORMATION TECHNOLOGIES Structure Theorems for Real- Time Variable Rate Coding With and Without Side Information

The output of a discrete Markov source is to be encoded instantaneously by a variable–rate encoder and decoded by a finite–state decoder. Our performance measure is a linear combination of the distortion and the instantaneous rate. Structure theorems, pertaining to the encoder and next–state functions are derived for every given finite–state decoder, which can have access to side information.