Attracted by its scalability towards practical code-word lengths, we revisit the idea of Turbo-
autoencoders for end-to-end learning of PHY-Layer communications. For this, we study the …

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 …

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 …

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 which …

This paper enhances the performance and training of the Turbo Autoencoder (TurboAE), an
end-to-end jointly trained neural channel encoder and decoder. A novel interleaver for …

A critical aspect of reliable communication involves the design of codes that allow
transmissions to be robustly and computationally efficiently decoded under noisy conditions …

End-to-end learning of a communications system using the deep learning-based
autoencoder concept has drawn interest in recent research due to its simplicity, flexibility …

Present-day communication systems routinely use codes that approach the channel
capacity when coupled with a computationally efficient decoder. However, the decoder is …

Channel Coding has been one of the central disciplines driving the success stories of
current generation LTE systems and beyond. In particular, turbo codes are mostly used for …

Isolated training with Gaussian priors (TGP) of the component autoencoders of turbo-
autoencoder architectures enables faster, more consistent training and better generalization …