Autoencoders (AEs) have been proposed to learn the physical layer of wireless
communication systems in an end-to-end fashion. However, it is challenging to jointly train …

Autoencoders are particularly interesting deep learning models for communications, as they
resemble the architecture of a classical transmission system. Several works explored the …

In this paper, a deeper autoencoder is proposed, which is composed of systematic and
strategic convolutional neural network (CNN) layers. The proposed autoencoder can …

We extend the idea of end-to-end learning of communications systems through deep neural
network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) …

The concept of using autoencoders (AEs) to represent wireless communication systems as
an end-to-end reconstruction task that optimizes the transmitter and receiver components …

Recent advancements in deep learning have led to the emergence of autoencoder-based
(AE) wireless communication systems, presenting a promising approach to tackle the …

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising,
and potentially disruptive Physical Layer (PHY) design for beyond-5G communication …

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 …

The end-to-end autoencoder is a novel and attracting concept to innovate communication
system architecture. In its training stage, the end-to-end autoencoder needs differentiable …

The application of Deep learning (DL) in wireless communications has achieved remarkable
success. However, there are still marked challenges impeding its use in the physical layer …