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 …

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 …

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 …

We address the problem of learning an efficient and adaptive physical layer encoding to
communicate binary information over an impaired channel. In contrast to traditional work, we …

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 …

In this paper, we present a learning scheme for Joint Source-Channel Coding (JSCC) over
analog independent additive noise channels. We formulate the learning problem by …

The autoencoder concept has fostered the reinterpretation and the design of modern
communication systems. It consists of an encoder, a channel and a decoder block that …

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 …

Error correction code (ECC) is an integral part of the physical communication layer, ensuring
reliable data transfer over noisy channels. Recently, neural decoders have demonstrated …

When a channel model is available, learning how to communicate on fading noisy channels
can be formulated as the (unsupervised) training of an autoencoder consisting of the …