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

The recent development in machine learning, especially in deep neural networks (DNN),
has enabled learning-based end-to-end communication systems, where DNNs are …

End-to-end learning of communications systems is a fascinating novel concept that has so
far only been validated by simulations for block-based transmissions. It allows learning of …

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 …

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 …

The idea of end-to-end learning of communication systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …

Recent years have witnessed growing interest in the application of deep neural networks
(DNNs) for receiver design, which can potentially be applied in complex environments …

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 …

Traditional communication system design has always been based on the paradigm of first
establishing a mathematical model of the communication channel, then designing and …