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 …

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO)
systems has been shown to have the potential of exceeding the performance of engineered …

We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output
(MIMO) communications based on unsupervised deep learning using an autoencoder. This …

We introduce a novel physical layer scheme for Multiple Input Multiple Output (MIMO)
communications based on unsupervised deep learning using an autoencoder. This method …

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 …

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 …

An end-to-end communications system based on Orthogonal Frequency Division
Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding …

We propose a deep learning-based channel estimation, quantization, feedback, and
precoding method for downlink multiuser multiple-input and multiple-output systems. In 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 …

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) …