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 …

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

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 …

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 …

Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …

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

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) …

Wireless communications are envisioned to bring about dramatic changes in the future, with
a variety of emerging applications, such as virtual reality, Internet of Things, and so on …

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 …

Bringing the success of modern machine learning (ML) techniques to mobile devices can
enable many new services and businesses, but also poses significant technical and …