Channel coding and modulation are two fundamental building blocks of physical layer
wireless communications. We propose a neural network based end-to-end communication …

In this article, we investigate deep auto-encoders for channel coding to combat the curse of
dimensionality commonly existing in learning based coding. Inspired by convolutional …

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 …

A variety of deep learning schemes have endeavoured to integrate deep neural networks
(DNNs) into channel coded systems by jointly designing DNN and the channel coding …

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 …

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 …

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on
fading channels, where both modulation and demodulation blocks are modeled by deep …

Deep learning has been recently applied to many problems in wireless communications
including modulation classification and symbol decoding. Many of the existing end-to-end …

Deep learning has been applied in physical-layer communications systems in recent years
and has demonstrated fascinating results that were comparable or even better than human …

Upcoming modern communications are based on 5G specifications and aim at providing
solutions for novel vertical industries. One of the major changes of the physical layer is the …