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 …

The existing end-to-end (E2E) wireless communication systems require fewer
communication modules and have a simple processing signal flow, compared to …

Wireless communications systems are traditionally designed by independently optimising
signal processing functions based on a mathematical model. Deep learning-enabled …

This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically
transmit short packets with few pilot symbols over a fading channel. Devices 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 …

Deep learning has solved many problems that are out of reach of heuristic algorithms. It has
also been successfully applied in wireless communications, even though the current radio …

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on
fading channels, where both modulation and demodulation blocks are modeled by deep …

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 autoencoder (AE) learning has the potential of exceeding the performance of
human-engineered transceivers and encoding schemes, without a priori knowledge of …

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) …