For reliable transmission across a noisy communication channel, classical results from
information theory show that it is asymptotically optimal to separate out the source and …

For reliable transmission across a noisy communication channel, classical results from
information theory show that it is asymptotically optimal to separate out the source and …

Although Shannon theory states that it is asymptotically optimal to separate the source and
channel coding as two independent processes, in many practical communication scenarios …

Error correction codes are a crucial part of the physical communication layer, ensuring the
reliable transfer of data over noisy channels. The design of optimal linear block codes …

Error correction code is a major part of the physical communication layer, ensuring the
reliable transfer of data over noisy channels. Recently, neural decoders were shown to …

Designing codes that combat the noise in a communication medium has remained a
significant area of research in information theory as well as wireless communications …

There have been significant research activities in recent years to automate the design of
channel encoders and decoders via deep learning. Due the dimensionality challenge in …

End-to-end deep learning for communication systems, ie, systems whose encoder and
decoder are learned, has attracted significant interest recently, due to its performance which …

Coding theory is a central discipline underpinning wireline and wireless modems that are
the workhorses of the information age. Progress in coding theory is largely driven by …

We consider the design of efficient neural-network based algorithms, referred to as neural
decoders, for decoding linear and non-linear block codes, such as Hamming and constant …