This paper deals with the use of emerging deep learning techniques in future wireless
communication networks. It will be shown that the data-driven approaches should not …

Given the unprecedented availability of data and computing resources, there is widespread
renewed interest in applying data-driven machine learning methods to problems for which …

Intelligent communication is gradually becoming a mainstream direction. As a major branch
of machine learning, deep learning (DL) has been applied in physical layer communications …

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 …

The communication industry is rapidly advancing towards 5G and beyond 5G (B5G) wireless
technologies in order to fulfill the ever-growing needs for higher data rates and improved …

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 …

Recently, in order to achieve performance improvement in scenarios where the channel is
either unknown, or too complex for an analytical description, Neural Network (NN) based …

We propose and practically demonstrate a joint detection and decoding scheme for short-
packet wireless communications in scenarios that require to first detect the presence of a …

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an
effective technique to reduce the power consumption and hardware complexity of wireless …

Deep neural networks (DNNs) allow digital receivers to learn to operate in complex
environments. To do so, DNNs should preferably be trained using large labeled data sets …