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

• Published in: IEEE transactions on cognitive communications and networking Vol. 4; no. 4; pp. 648 - 664
• Main Author: Simeone, Osvaldo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Cloud computing; Communication networks; Communications systems; Decoding; Machine learning; Machine learning algorithms; Mathematical model; supervised learning; Task analysis; Training; Unsupervised learning; wireless communications
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2018.2881442

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 modeling 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, with an emphasis on the physical layer.