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 …

Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

End-to-end learning of communication systems enables joint optimization of transmitter and
receiver, implemented as deep neural network (NN)-based autoencoders, over any type of …

Intelligent communication is gradually becoming a mainstream direction. As a major branch
of machine learning, deep learning (DL) has been applied in physical layer communications …

In this paper, we study the deep learning (DL) based end-to-end transmission systems, then
we present the analysis for the underfitting and overfitting phenomena which happen during …

In this article, we develop an end-to-end wireless communication system using deep neural
networks (DNNs), where DNNs are employed to perform several key functions, including …

Recent developments in applying deep learning techniques to train end-to-end
communication systems have shown great promise in improving the overall performance of …

In this article, we use deep neural networks (DNNs) to develop an end-to-end wireless
communication system, in which DNNs are employed for all signal-related functionalities …

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 …

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 …