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 …

The problem of computing sum of sources over a multiple access channel (MAC) is
considered. Building on the technique of linear computation coding (LCC) proposed by …

We consider the problem of communication over a three-user discrete memoryless
interference channel (3-IC). The current known coding techniques for communicating over …

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 …

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 …