A Very Brief Introduction to Machine Learning With Applications to Communication Systems

TL;DR

This paper provides a high-level overview of machine learning techniques and their applications in communication systems, emphasizing when and why data-driven methods are beneficial over traditional approaches.

Contribution

It offers an accessible introduction to supervised and unsupervised learning with specific examples in communication networks, bridging theory and practical applications.

Findings

ML techniques are increasingly useful in communication systems.

Applications span edge and cloud segments of networks.

Tutorial-style overview aids understanding of ML in communications.

Abstract

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 the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack.