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 communications systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …

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 …

Recently, deep learning based end-to-end communication systems have been developed,
where both the transmitter and the receiver are represented as deep neural networks (DNN) …

The design and implementation of conventional communication systems are based on
strong probabilistic models and assumptions. These fixed and conventional communication …

When a channel model is available, learning how to communicate on fading noisy channels
can be formulated as the (unsupervised) training of an autoencoder consisting of the …

When a channel model is not available, the end-to-end training of encoder and decoder on
a fading noisy channel generally requires the repeated use of the channel and of a feedback …

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 …

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 …

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) …