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 …

A variety of deep learning schemes have endeavoured to integrate deep neural networks
(DNNs) into channel coded systems by jointly designing DNN and the channel coding …

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for
uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent …

Channel coding and modulation are two fundamental building blocks of physical layer
wireless communications. We propose a neural network based end-to-end communication …

End-to-end learning of a communications system using the deep learning-based
autoencoder concept has drawn interest in recent research due to its simplicity, flexibility …

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple
access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA …

Codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be
considered as a multi-user multi-dimensional modulation (MU-MDM) design. However, the …

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 consider a trainable point-to-point communication system, where both transmitter and
receiver are implemented as neural networks (NNs), and demonstrate that training on the bit …

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 …