Shannon's channel coding theorem characterizes the maximal rate of information that can
be reliably transmitted over a communication channel when optimal encoding and decoding …

Shannon propounded a theoretical framework (collectively called information theory) that
uses mathematical tools to understand, model and analyze modern mobile wireless …

This paper considers the joint compression of a pair of correlated sources, where the
encoder is allowed to access only one of the sources. The objective is to recover both …

We study the mismatched successive refinement problem where one uses Gaussian
codebooks to compress an arbitrary memoryless source with successive minimum …

We consider the distributional connection between the lossy compressed representation of a
high-dimensional signal X using a random spherical code and the observation of X under an …

Theoretical and experimental results have shown that compressed sensing with
quantization can perform well if the signal is very sparse, the noise is very low, and the …

We study the two-user asymmetric broadcast channel with additive non-Gaussian noise and
derive a second-order achievability rate region when separate error probabilities constraints …

We revisit the high-dimensional content identification with lossy recovery problem (Tuncel
and Gündüz, 2014) and establish an exponential strong converse theorem. As a corollary of …

We consider a joint source channel coding (JSCC) problem in which we desire to transmit
an arbitrary memoryless source over an arbitrary additive channel. We propose a …

This paper studies a joint data and semantics lossy compression problem in the finite
blocklength regime, where the data and semantic sources are correlated, and only the data …