The compute-and-forward framework permits each receiver in a Gaussian network to directly
decode a linear combination of the transmitted messages. The resulting linear combinations …

In this monograph, we develop a mathematical framework based on asymptotically good
random structured codes, ie, codes possessing algebraic properties, for network information …

This paper considers the problem of distributed lossy compression where the goal is to
recover one or more linear combinations of the sources at the decoder, subject to distortion …

This paper presents a joint typicality framework for encoding and decoding nested linear
codes in multi-user networks. This framework provides a new perspective on compute …

Algebraic network information theory is an emerging facet of network information theory,
studying the achievable rates of random code ensembles that have algebraic structure, such …

Compute–forward is a coding technique that enables receiver (s) in a network to directly
decode one or more linear combinations of the transmitted codewords. Initial efforts focused …

We consider the problem of developing coding techniques and deriving achievable rate
regions for discrete memoryless broadcast channels with three receivers (3-DBC). We begin …

We consider the task of faithfully simulating a distributed quantum measurement and
function computation, and demonstrate a new achievable rate-region. For this, we develop …

In this paper, we study the asymptotic performance of Abelian group codes for the channel
coding problem for arbitrary discrete (finite alphabet) memoryless channels as well as the …

This paper presents a joint typicality framework for encoding and decoding nested linear
codes for multi-user networks. This framework provides a new perspective on compute …