Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

We present and discuss several novel applications of deep learning for the physical layer.
By interpreting a communications system as an autoencoder, we develop a fundamental …

The commercial fifth-generation (5G) wireless communications networks have already been
deployed with the aim of providing high data rates. However, the rapid growth in the number …

Wireless powered mobile-edge computing (MEC) has recently emerged as a promising
paradigm to enhance the data processing capability of low-power networks, such as …

In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere
at an of altitude around 20 km and is instrumental for providing communication services …

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 …

As a promising machine learning tool to handle the accurate pattern recognition from
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …

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 …